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Randomized Sensor Selection in Sequential Hypothesis Testing

We consider the problem of sensor selection for time-optimal detection of a hypothesis. We consider a group of sensors transmitting their observations to a fusion center. The fusion center considers the output of only one randomly chosen sensor at the time, and performs a sequential hypothesis test. We consider the class of sequential tests which are easy to implement, asymptotically optimal, and computationally amenable. For three distinct performance metrics, we show that, for a generic set of sensors and binary hypothesis, the fusion center needs to consider at most two sensors. We also show that for the case of multiple hypothesis, the optimal policy needs at most as many sensors to be observed as the number of underlying hypotheses.


I. INTRODUCTION
In today's information-rich world, different sources are best informers about different topics. If the topic under consideration is well known beforehand, then one chooses the best source. Otherwise, it is not obvious what source or how many sources one should observe. This need to identify sensors (information sources) to be observed in decision making problems is found in many common situations, e.g., when deciding which news channel to follow. When a person decides what information source to follow, she relies in general upon her experience, i.e., one knows through experience what combination of news channels to follow.
In engineering applications, a reliable decision on the underlying hypothesis is made through repeated measurements. Given infinitely many observations, decision making can be performed accurately. Given a cost associated to each observation, a well-known tradeoff arises between accuracy and number of iterations. Various sequential hypothesis tests have been proposed to detect the underlying hypothesis within a given degree of accuracy. There exist two different classes of sequential tests. The first class includes sequential tests developed from the dynamic programming point of view. These tests are optimal and, in general, difficult to implement [5]. The second class consists of easily-implementable and asymptotically-optimal sequential tests; a widely-studied example is the Sequential Probability Ratio Test (SPRT) for binary hypothesis testing and its extension, the Multihypothesis Sequential Probability Ratio Test (MSPRT).
In this paper, we consider the problem of quickest decision making using sequential probability ratio tests. Recent advances in cognitive psychology [7] show that the performance of a human performing decision making tasks, such as "twoalternative forced choice tasks," is well modeled by a drift diffusion process, i.e., the continuous-time version of SPRT. Roughly speaking, modeling decision making as an SPRT process is somehow appropriate even for situations in which a human is making the decision.
Sequential hypothesis testing and quickest detection problems have been vastly studied [17], [4]. The SPRT for binary decision making was introduced by Wald in [21], and was extended by Armitage to multiple hypothesis testing in [1]. The Armitage test, unlike the SPRT, is not necessarily optimal [24]. Various other tests for multiple hypothesis testing have been developed throughout the years; a survey is presented in [18]. Designing hypothesis tests, i.e., choosing thresholds to decide within a given expected number of iterations, through any of the procedures in [18] is infeasible as none of them provides any results on the expected sample size. A sequential test for multiple hypothesis testing was developed in [5], [10], and [11], which provides with an asymptotic expression for the expected sample size. This sequential test is called the MSPRT and reduces to the SPRT in case of binary hypothesis.
Recent years have witnessed a significant interest in the problem of sensor selection for optimal detection and estimation. Tay et al [20] discuss the problem of censoring sensors for decentralized binary detection. They assess the quality of sensor data by the Neyman-Pearson and a Bayesian binary hypothesis test and decide on which sensors should transmit their observation at that time instant. Gupta et al [13] focus on stochastic sensor selection and minimize the error covariance of a process estimation problem. Isler et al [14] propose geometric sensor selection schemes for error minimization in target detection. Debouk et al [9] formulate a Markovian decision problem to ascertain some property in a dynamical system, and choose sensors to minimize the associated cost. Wang et al [22] design entropy-based sensor selection algorithms for target localization. Joshi et al [15] present a convex optimization-based heuristic to select multiple sensors for optimal parameter estimation. Bajović et al [3] discuss sensor selection problems for Neyman-Pearson binary hypothesis testing in wireless sensor networks.
A third and last set of references related to this paper are those on linear-fractional programming. Various iterative and cumbersome algorithms have been proposed to optimize linear-fractional functions [8], [2]. In particular, for the problem of minimizing the sum and the maximum of linear-fractional functionals, some efficient iterative algorithms have been proposed, including the algorithms by Falk et al [12] and by Benson [6].
In this paper, we analyze the problem of time-optimal sequential decision making in the presence of multiple switching sensors and determine a sensor selection strategy to achieve the same. We consider a sensor network where all sensors are connected to a fusion center. The fusion center, at each instant, receives information from only one sensor. Such a situation arises when we have interfering sensors (e.g., sonar sensors), a fusion center with limited attention or information processing capabilities, or sensors with shared communication resources. The fusion center implements a sequential hypothesis test with the gathered information. We consider two such tests, namely, the SPRT and the MSPRT for binary and multiple hypothesis, respectively. First, we develop a version of the SPRT and the MSPRT where the sensor is randomly switched at each iteration, and determine the expected time that these tests require to obtain a decision within a given degree of accuracy. Second, we identify the set of sensors that minimize the expected decision time. We consider three different cost functions, namely, the conditioned decision time, the worst case decision time, and the average decision time. We show that the expected decision time, conditioned on a given hypothesis, using these sequential tests is a linear-fractional function defined on the probability simplex. We exploit the special structure of our domain (probability simplex), and the fact that our data is positive to tackle the problem of the sum and the maximum of linear-fractional functionals analytically. Our approach provides insights into the behavior of these functions. The major contributions of this paper are: i) We develop a version of the SPRT and the MSPRT where the sensor is selected randomly at each observation. ii) We determine the asymptotic expressions for the thresholds and the expected sample size for these sequential tests. iii) We incorporate the processing time of the sensors into these models to determine the expected decision time. iv) We show that, to minimize the conditioned expected decision time, the optimal policy requires only one sensor to be observed. v) We show that, for a generic set of sensors and M underlying hypotheses, the optimal average decision time policy requires the fusion center to consider at most M sensors. vi) For the binary hypothesis case, we identify the optimal set of sensors in the worst case and the average decision time minimization problems. Moreover, we determine an optimal probability distribution for the sensor selection. vii) In the worst case and the average decision time minimization problems, we encounter the problem of minimization of sum and maximum of linear-fractional functionals. We treat these problems analytically, and provide insight into their optimal solutions.
The remainder of the paper is organized in following way. In Section II, we present the problem setup. Some preliminaries are presented in Section III. We develop the switching-sensor version of the SPRT and the MSPRT procedures in Section IV. In Section V, we formulate the optimization problems for time-optimal sensor selection, and determine their solution. We elucidate the results obtained through numerical examples in Section VI. Our concluding remarks are in Section VII.

II. PROBLEM SETUP
We consider a group of n > 1 agents (e.g., robots, sensors, or cameras), which take measurements and transmit them to a fusion center. We generically call these agents "sensors." We identify the fusion center with a person supervising the agents, and call it "the supervisor." The goal of the supervisor is to decide, based on the measurements it receives, which of the M ≥ 2 alternative hypotheses or "states of nature," H k , k ∈ {0, . . . , M − 1} is correct. For doing so, the supervisor uses sequential hypothesis tests, which we briefly review in the next section.
We assume that only one sensor can transmit to the supervisor at each (discrete) time instant. Equivalently, the supervisor can process data from only one of the n agents at each time. Thus, at each time, the supervisor must decide which sensor should transmit its measurement. We are interested in finding the optimal sensor(s), which must be observed in order to minimize the decision time.
We model the setup in the following way: i) Let s l ∈ {1, . . . , n} indicate which sensor transmits its measurement at time instant l ∈ N. ii) Conditioned on the hypothesis H k , k ∈ {0, . . . , M − 1}, the probability that the measurement at sensor s is y, is denoted by f k s (y). iii) The prior probability of the hypothesis H k , k ∈ {0, . . . , M − 1}, being correct is π k . iv) The measurement of sensor s at time l is y s l . We assume that, conditioned on hypothesis H k , y s l is independent of y(sl), for (l, s l ) = (l, sl). v) The time it takes for sensor s to transmit its measurement (or for the supervisor to process it) is T s > 0. vi) The supervisor chooses a sensor randomly at each time instant; the probability to choose sensor s is stationary and given by q s . vii) The supervisor uses the data collected to execute a sequential hypothesis test with the desired probability of incorrect decision, conditioned on hypothesis H k , given by α k .
viii) We assume that there are no two sensors with identical conditioned probability distribution f k s (y) and processing time T s . If there are such sensors, we club them together in a single node, and distribute the probability assigned to that node equally among them.

A. Linear-fractional function
is called a linear-fractional function [8]. A linear-fractional function is quasi-convex as well as quasi-concave. In particular, if q = 1, then any scalar linear-fractional function g satisfies

B. Kullback-Leibler distance
Given two probability distributions functions Further, D(f 1 , f 2 ) ≥ 0, and the equality holds if and only if f 1 = f 2 almost everywhere.

C. Sequential Probability Ratio Test
The SPRT is a sequential binary hypothesis test that provides us with two thresholds to decide on some hypothesis, opposed to classical hypothesis tests, where we have a single threshold. Consider two hypothesis H 0 and H 1 , with prior probabilities π 0 and π 1 , respectively. Given their conditional probability distribution functions f (y|H 0 ) = f 0 (y) and f (y|H 1 ) = f 1 (y), and repeated measurements {y 1 , y 2 , . . .}, with λ 0 defined by the SPRT provides us with two constants η 0 and η 1 to decide on a hypothesis at each time instant l, in the following way: i) Compute the log likelihood ratio: ii) Integrate evidence up to time N , i.e., Λ N : iii) Decide only if a threshold is crossed, i.e., Given the probability of false alarm P(H 1 |H 0 ) = α 0 and probability of missed detection P(H 0 |H 1 ) = α 1 , the Wald's thresholds η 0 and η 1 are defined by The expected sample size N , for decision using SPRT is asymptotically given by as −η 0 , η 1 → ∞. The asymptotic expected sample size expressions in equation (4) are valid for large thresholds. The use of these asymptotic expressions as approximate expected sample size is a standard approximation in the information theory literature, and is known as Wald's approximation [4], [17], [19]. For given error probabilities, the SPRT is the optimal sequential binary hypothesis test, if the sample size is considered as the cost function [19].

D. Multi-hypothesis Sequential Probability Ratio Test
The MSPRT for multiple hypothesis testing was introduced in [5], and was further generalized in [10] and [11]. It is described as follows. Given M hypotheses with their prior probabilities π k , k ∈ {0, . . . , M − 1}, the posterior probability after N observations y l , l ∈ {1, . . . , N } is given by where f k is the probability density function of the observation of the sensor, conditioned on hypothesis k.
Before we state the MSPRT, for a given N , we definek bȳ The MSPRT at each sampling iteration l is defined as where the thresholds η k , for given frequentist error probabilities (accept a given hypothesis wrongly) α k , k ∈ {0, . . . , M − 1}, are given by where γ k ∈ ]0, 1[ is a constant function of f k (see [5]). It can be shown [5] that the expected sample size of the MSPRT, conditioned on a hypothesis, satisfies The MSPRT is an easily-implementable hypothesis test and is shown to be asymptotically optimal in [5], [10].

A. SPRT with switching sensors
Consider the case when the fusion center collects data from n sensors. At each iteration the fusion center looks at one sensor chosen randomly with probability q s , s ∈ {1, . . . , n}. The fusion center performs SPRT with the collected data. We define this procedure as SPRT with switching sensors. If we assume that sensor s l is observed at iteration l, and the observed value is y s l , then SPRT with switching sensors is described as following, with the thresholds η 0 and η 1 defined in equation (3), and λ 0 defined in equation (2): i) Compute log likelihood ratio:

Lemma 1 (Expected sample size):
For the SPRT with switching sensors described above, the expected sample size conditioned on a hypothesis is asymptotically given by: , and as −η 0 , η 1 → ∞. Proof: Similar to the proof of Theorem 3.2 in [23]. The expected sample size converges to the values in equation (6) for large thresholds. From equation (3), it follows that large thresholds correspond to small error probabilities. In the remainder of the paper, we assume that the error probabilities are chosen small enough, so that the above asymptotic expression for sample size is close to the actual expected sample size.
Lemma 2 (Expected decision time): Given the processing time of the sensors T s , s ∈ {1, . . . , n}, the expected decision time of the SPRT with switching sensors T d , conditioned on the hypothesis H k , k ∈ {0, 1}, is where T, I k ∈ R n >0 , are constant vectors for each k ∈ {0, 1}. Proof: The decision time using SPRT with switching sensors is the sum of sensor's processing time at each iteration. We observe that the number of iterations in SPRT and the processing time of sensors are independent. Hence, the expected value of the decision time T d is By the definition of expected value, From equations (6), (8), and (9) it follows that where I k s ∈ R >0 is a constant, for each k ∈ {0, 1}, and s ∈ {1, . . . , n}.

B. MSPRT with switching sensors
We call the MSPRT with the data collected from n sensors while observing only one sensor at a time as the MSPRT with switching sensors. The one sensor to be observed at each time is determined through a randomized policy, and the probability of choosing sensor s is stationary and given by q s . Assume that the sensor s l ∈ {1, . . . , n} is chosen at time instant l, and the prior probabilities of the hypothesis are given by π k , k ∈ {0, . . . , M − 1}, then the posterior probability after N observations y l , l ∈ {1, . . . , N } is given by , Before we state the MSPRT with switching sensors, for a given N , we definek bỹ For the thresholds η k , k ∈ {0, . . . , M − 1}, defined in equation (5), the MSPRT with switching sensors at each sampling iteration N is defined by p k n > 1 1+η k , for at least one k, say Hk, otherwise, continue sampling.
Before we state the results on asymptotic sample size and expected decision time, we introduce the following notation. For a given hypothesis H k , and a sensor s, we define j * We also define E D : ∆ n−1 × (L 1 ) n × (L 1 ) n → R by where ∆ n−1 represents the probability simplex in R n .
Proof: The proof follows from Theorem 5.1 of [5] and the observation that

Lemma 4 (Expected decision time):
Given the processing time of the sensors T s , s ∈ {1, . . . , n}, the expected decision time T d conditioned on the hypothesis H k , for each k ∈ {0, . . . , M − 1}, is given by where T,Ī k ∈ R n >0 are constants. Proof: Similar to the proof of Lemma 2.

V. OPTIMAL SENSOR SELECTION
In this section we consider sensor selection problems with the aim to minimize the expected decision time of a sequential hypothesis test with switching sensors. As exemplified in Lemma 4, the problem features multiple conditioned decision times and, therefore, multiple distinct cost functions are of interest. In Scenario I below, we aim to minimize the decision time conditioned upon one specific hypothesis being true; in Scenarios II and III we will consider worst-case and average decision times. In all three scenarios the decision variables take values in the probability simplex.
Minimizing decision time conditioned upon a specific hypothesis may be of interest when fast reaction is required in response to the specific hypothesis being indeed true. For example, in change detection problems one aims to quickly detect a change in a stochastic process; the CUSUM algorithm (also referred to as Page's test) [16] is widely used in such problems. It is known [4] that, with fixed threshold, the CUSUM algorithm for quickest change detection is equivalent to an SPRT on the observations taken after the change has occurred. We consider the minimization problem for a single conditioned decision time in Scenario I below and we show that, in this case, observing the best sensor each time is the optimal strategy.
In general, no specific hypothesis might play a special role in the problem and, therefore, it is of interest to simultaneously minimize multiple decision times over the probability simplex. This is a multi-objective optimization problem, and may have Pareto-optimal solutions. We tackle this problem by constructing a single aggregate objective function. In the binary hypothesis case, we construct two single aggregate objective functions as the maximum and the average of the two conditioned decision times. These two functions are discussed in Scenario II and Scenario III respectively. In the multiple hypothesis setting, we consider the single aggregate objective function constructed as the average of the conditioned decision times. An analytical treatment of this function for M > 2, is difficult. We determine the optimal number of sensors to be observed, and direct the interested reader to some iterative algorithms to solve such optimization problems. This case is also considered under Scenario III.
Before we pose the problem of optimal sensor selection, we introduce the following notation. We denote the probability simplex in R n by ∆ n−1 , and the vertices of the probability simplex ∆ n−1 by e i , i ∈ {1, . . . , n}. We refer to the line joining any two vertices of the simplex as an edge. Finally, we define g k : ∆ n−1 → R, k ∈ {0, . . . , M − 1}, by

A. Scenario I (Optimization of conditioned decision time):
We consider the case when the supervisor is trying to detect a particular hypothesis, irrespective of the present hypothesis. The corresponding optimization problem for a fixed k ∈ {0, . . . , M − 1} is posed in the following way: The solution to this minimization problem is given in the following theorem.

Theorem 1 (Optimization of conditioned decision time):
The solution to the minimization problem (11) is q * = e s * , where s * is given by Proof: We notice that objective function is a linearfractional function. In the following argument, we show that the minima occurs at one of the vertices of the simplex.
We first notice that the probability simplex is the convex hull of the vertices, i.e., any pointq in the probability simplex can be written as q = n s=1 α s e s , n s=1 α s = 1, and α s ≥ 0.
We invoke equation (1), and observe that for some β ∈ [0, 1] and for any s, r ∈ {1, . . . , n} which can be easily generalized to for any pointq in the probability simplex ∆ n−1 . Hence, minima will occur at one of the vertices e s * , where s * is given by

B. Scenario II (Optimization of the worst case decision time):
For the binary hypothesis testing, we consider the multiobjective optimization problem of minimizing both decision times simultaneously. We construct single aggregate objective function by considering the maximum of the two objective functions. This turns out to be a worst case analysis, and the optimization problem for this case is posed in the following way: Before we move on to the solution of above minimization problem, we state the following results.
Lemma 5 (Monotonicity of conditioned decision times): The functions g k , k ∈ {0, . . . , M − 1} are monotone on the probability simplex ∆ n−1 , in the sense that given two points q 1 , q 2 ∈ ∆ n−1 , the function g k is monotonically non-increasing or monotonically non-decreasing along the line joining q 1 and q 2 .
Proof: Consider probability vectors q 1 , q 2 ∈ ∆ n−1 . Any point q on line joining q 1 and q 2 can be written as q(t) = tq 1 + (1 − t)q 2 , t ∈ ]0, 1[. We note that g k (q(t)) is given by: The derivative of g k along the line joining q 1 and q 2 is given by We note that the sign of the derivative of g k along the line joining two points q 1 , q 2 is fixed by the choice of q 1 and q 2 . Hence, the function g k is monotone over the line joining q 1 and q 2 . Moreover, note that if g k (q 1 ) = g k (q 2 ), then g k is strictly monotone. Otherwise, g k is constant over the line joining q 1 and q 2 .
Lemma 6 (Location of min-max): Define g : ∆ n−1 → R ≥0 by g = max{g 0 , g 1 }. A minimum of g lies at the intersection of the graphs of g 0 and g 1 , or at some vertex of the probability simplex ∆ n−1 .
Proof: Case 1: The graphs of g 0 and g 1 do not intersect at any point in the simplex ∆ n−1 .
In this case, one of the functions g 0 and g 1 is an upper bound to the other function at every point in the probability simplex ∆ n−1 . Hence, g = g k , for some k ∈ {0, 1}, at every point in the probability simplex ∆ n−1 . From Theorem 1, we know that the minima of g k on the probability simplex ∆ n−1 lie at some vertex of the probability simplex ∆ n−1 .
Case 2: The graphs of g 0 and g 1 intersect at a set Q in the probability simplex ∆ n−1 , and letq be some point in the set Q.
We invoke Lemma 5, and notice that g 0 and g 1 can intersect at most once on a line. Moreover, we note that g 0 (q * ) > g 1 (q * ), hence, along the half-line fromq through q * , g 0 > g 1 , that is, g = g 0 . Since g 0 (q * ) < g 0 (q), g is decreasing along this half-line. Hence, g should achieve its minimum at the boundary of the simplex ∆ n−1 , which contradicts that q * is in the relative interior of the simplex ∆ n−1 . In summary, if a minimum of g lies in the relative interior of the probability simplex ∆ n−1 , then it lies at the intersection of the graphs of g 0 and g 1 .
The same argument can be applied recursively to show that if a minimum lies at some point q † on the boundary, then either g 0 (q † ) = g 1 (q † ) or the minimum lies at the vertex.
In the following arguments, let Q be the set of points in the simplex ∆ n−1 , where g 0 = g 1 , that is, Also notice that the set Q is non empty if and only if I 0 −I 1 has at least one non-negative and one non-positive entry. If the set Q is empty, then it follows from Lemma 6 that the solution of optimization problem in equation (15) lies at some vertex of the probability simplex ∆ n−1 . Now we consider the case when Q is non empty. We assume that the sensors have been reordered such that the entries in I 0 − I 1 are in ascending order. We further assume that, for I 0 −I 1 , the first m entries, m < n, are non positive, and the remaining entries are positive.
Lemma 7 (Intersection polytope): If the set Q defined in equation (16) is non empty, then the polytope generated by the points in the set Q has vertices given by: Proof: Any q ∈ Q satisfies the following constraints Eliminating q n , using equation (18) and equation (19), we get: .
The equation (20) defines a hyperplane, whose extreme points in R n−1 ≥0 are given bỹ Note that for s ∈ {1, . . . , m},q sn ∈ ∆ n−1 . Hence, these points define some vertices of the polytope generated by points in the set Q. Also note that the other vertices of the polytope can be determined by the intersection of each pair of lines 1 2 (g 0 (q(t)) + g 1 (q(t))).

Theorem 3 (Optimization of average decision time):
For the optimization problem in equation (21) with M = 2, the following statements hold: i) If I 0 , I 1 are linearly dependent, then the solution lies at some vertex of the simplex ∆ n−1 . ii) If I 0 and I 1 are linearly independent, and T = α 0 I 0 + α 1 I 1 , α 0 , α 1 ∈ R, then the following statements hold: a) If α 0 and α 1 have opposite signs, then the optimal solution lies at some edge of the simplex ∆ n−1 . b) If α 0 , α 1 > 0, then the optimal solution may lie in the interior of the simplex ∆ n−1 . iii) If every 3 × 3 sub-matrix of the matrix T I 0 I 1 ∈ R n×3 is full rank, then a minimum lies at an edge of the simplex ∆ n−1 . Proof: We start by establishing the first statement. Since, I 0 and I 1 are linearly dependent, there exists a γ > 0 such that I 0 = γI 1 . For I 0 = γI 1 , we have g 0 + g 1 = (1 + γ)g 0 . Hence, the minima of g 0 + g 1 lies at the same point where g 0 achieves the minima. From Theorem 1, it follows that g 0 achieves the minima at some vertex of the simplex ∆ n−1 .
To prove the second statement, we note that from Lemma 9, it follows that if α 0 , and α 1 have opposite signs, then the Jacobian of g 0 + g 1 does not vanish anywhere on the simplex ∆ n−1 . Hence, the minima lies at the boundary of the simplex. Notice that the boundary, of the simplex ∆ n−1 , are n simplices ∆ n−2 . Notice that the argument repeats till n > 2. Hence, the optima lie on one of the n 2 simplices ∆ 1 , which are the edges of the original simplex. Moreover, we note that from Lemma 9, it follows that if α 0 , α 1 > 0, then we can not guarantee the number of optimal set of sensors. This concludes the proof of the second statement.
To prove the last statement, we note that it follows immediately from Lemma 10 that a solution of the optimization problem in equation (21) would lie at some simplex ∆ 1 , which is an edge of the original simplex.
Note that, we have shown that, for M = 2 and a generic set of sensors, the solution of the optimization problem in equation (21) lies at an edge of the simplex ∆ n−1 . The optimal value of the objective function on a given edge was determined in Lemma 11. Hence, an optimal solution of this problem can be determined by a comparison of the optimal values at each edge.
For the multiple hypothesis case, we have determined the time-optimal number of the sensors to be observed in Lemma 10. In order to identify these sensors, one needs to solve the optimization problem in equation (21). We notice that the objective function in this optimization problem is nonconvex, and is hard to tackle analytically for M > 2. Interested reader may refer to some efficient iterative algorithms in linear-fractional programming literature (e.g., [6]) to solve these problems.

VI. NUMERICAL EXAMPLES
We consider four sensors connected to a fusion center. We assume that the sensors take binary measurements. The probabilities of their measurement being zero, under two hypotheses, and their processing times are given in the Table I.  We performed Monte-Carlo simulations with the misdetection and false-alarm probabilities fixed at 10 −3 , and computed expected decision times. In Table II, the numerical expected decision times are compared with the decision times obtained analytically in equation (7). The difference in the numerical and the analytical decision times is explained by the Wald's asymptotic approximations. In the Table III, we compare optimal policies in each Scenario I, II, and III with the policy when each sensor is chosen uniformly. It is observed that the optimal policy improves the expected decision time significantly over the uniform policy. We performed another set of simulations for the multihypothesis case. We considered a ternary detection problem, where the underlying signal x = 0, 1, 2 needs to be detected from the available noisy data. We considered a set of four sensors and their conditional probability distribution is given in Tables IV and V. The processing time of the sensors were chosen to be the same as in Table I.
The set of optimal sensors were determined for this set of data. Monte-Carlo simulations were performed with the thresholds η k , k ∈ {0, . . . , M − 1} set at 10 −6 . A comparison of the uniform sensor selection policy and an optimal sensor selection policy is presented in Table VI. Again, the significant difference between the average decision time in the uniform and the optimal policy is evident. We note that the optimal results, we obtained, may only be sub-optimal because of the asymptotic approximations in equations (3) and (5). We further note that, for small error probabilities and large sample sizes, these asymptotic approximations yield fairly accurate results [5], and in fact, this is the regime in which it is of interest to minimize the expected decision time. Therefore, for all practical purposes the obtained optimal scheme is very close to the actual optimal scheme.

VII. CONCLUSIONS
In this paper, we considered the problem of sequential decision making. We developed versions SPRT and MSPRT where the sensor switches at each observation. We used these sequential procedures to decide reliably. We found out the set of optimal sensors to decide in minimum time. For the binary hypothesis case, three performance metrics were considered and it was found that for a generic set of sensors at most two sensors are optimal. Further, it was shown that for M underlying hypotheses, and a generic set of sensors, an optimal policy requires at most M sensors to be observed. A procedure for identification of the optimal sensor was developed. In the binary hypothesis case, the computational complexity of the procedure for the three scenarios, namely, the conditioned decision time, the worst case decision time, and the average decision time, was O(n), O(n 2 ), and O(n 2 ), respectively.
Many further extensions to the results presented here are possible. First, the time-optimal scheme may not be energy optimal. In particular, the time optimal set of sensors may be the most distant sensors from the fusion center. Given that the power to transmit the signal to the fusion center is proportional to the distance from the fusion center, the time-optimal scheme is no where close to the energy optimal scheme. This trade off can be taken care of by adding a term proportional to distance in the objective function.
When we choose only one or two sensors every time, issues of robustness do arise. In case of sensor failure, we need to determine the next best sensor to switch. A list of sensors, with increasing decision time, could be prepared beforehand and in case of sensor failure, the fusion center should switch to the next best set of sensors.