Srikanth Hariharan

Maximizing aggregated revenue in sensor networks under deadline constraints

We study the problem of maximizing the aggregated revenue in sensor networks with deadline constraints. Our model is that of a sensor network that is arranged in the form of a tree topology, where the root corresponds to the sink node, and the rest of the network detects an event and transmits data to the sink over one or more hops. We assume a time-slotted synchronized system and a node-exclusive (also called a primary) interference model. We formulate this problem as an integer optimization problem and show that the optimal solution involves solving a Bipartite Maximum Weighted Matching problem at each hop. We propose a polynomial time algorithm based on dynamic programming that uses only local information at each hop to obtain the optimal solution. Thus, we answer the question of when a node should stop waiting to aggregate data from its predecessors and start transmitting in order to maximize revenue within a deadline imposed by the sink. Further, we show that our optimization framework is general enough that it can be extended to a number of interesting cases such as incorporating sleep-wake scheduling, minimizing aggregate sensing error, etc.

Maximizing Aggregated Information in Sensor Networks Under Deadline Constraints

We study the problem of maximizing the aggregated information in sensor networks with deadline constraints. Our model is that of a sensor network that is arranged in the form of a tree topology, where the root corresponds to the sink node, and the rest of the network detects an event and transmits data to the sink over one or more hops. We assume a time-slotted synchronized system and a node-exclusive (also called a primary) interference model. We formulate this problem as an integer optimization problem and show that for unit capacity links, the optimal solution involves solving a Bipartite Maximum Weighted Matching problem at each hop. We propose a polynomial time algorithm that uses only local information at each hop to obtain the optimal solution. Thus, we answer the question of when a node should stop waiting to aggregate data from its predecessors and start transmitting in order to maximize aggregated information within a deadline imposed by the sink. We extend our model to allow for practical considerations such as arbitrary link capacities, and also for multiple overlapping events. Further, we show that our framework is general enough that it can be extended to a number of interesting cases such as incorporating sleep-wake scheduling, minimizing aggregate sensing error, etc.

Deadline constrained scheduling for data aggregation in unreliable sensor networks

We study the problem of maximizing the aggregated information in a wireless sensor network. We consider a sensor network with a tree topology, where the root corresponds to the sink, and the rest of the network detects an event and transmits data to the sink. We formulate an integer optimization problem that maximizes the aggregated information that reaches the sink under deadline and interference constraints. This framework allows using a variety of error recovery schemes to tackle link unreliability. We show that the optimal solution involves solving a Job Interval Selection Problem (JISP) which is known to be MAX SNP-Hard. We construct a sub-optimal version, and develop a low complexity, distributed optimal solution to this version. We investigate tree structures for which this solution is optimal to the original problem. Our numerical results show that the sub-optimal solution outperforms existing JISP approximation algorithms even for general trees.

Maximizing Information in Unreliable Sensor Networks Under Deadline and Energy Constraints

We study the problem of maximizing the information in a wireless sensor network with unreliable links. We consider a sensor network with a tree topology, where the root corresponds to the sink, and the rest of the network detects an event and transmits data to the sink. We formulate a combinatorial optimization problem that maximizes the information that reaches the sink under deadline, energy, and interference constraints. This framework allows using a variety of error recovery schemes to tackle link unreliability. We show that this optimization problem is NP-hard in the strong sense when the input is the maximum node degree of the tree. We then propose a dynamic programming framework for solving the problem exactly, which involves solving a special case of the job interval selection problem (JISP) at each node. Our solution has a polynomial time complexity when the maximum node degree is O(logN) in a tree with N nodes. For trees with higher node degrees, we further develop a suboptimal solution, which has low complexity and allows distributed implementation. We investigate tree structures for which this solution is optimal to the original problem. The efficiency of the suboptimal solution is further demonstrated through numerical results on general trees.

Secure neighbor discovery through overhearing in static multihop wireless networks

In wireless ad-hoc and sensor networks, neighbor discovery is one of the first steps performed by a node upon deployment and disrupting it adversely affects a number of routing, MAC, topology discovery and intrusion detection protocols. It is especially harmful when an adversary can convince nodes that it is a legitimate neighbor, which it can do easily and without the use of cryptographic primitives. In this paper, we develop a secure neighbor discovery protocol, SEDINE, for static multihop wireless networks. We prove that, in the absence of packet losses, without using any centralized trusted node or specialized hardware, SEDINE prevents any node, legitimate or malicious, from being incorrectly added to the neighbor list of another legitimate node that is not within its transmission range. We provide simulation results to demonstrate the efficacy of SEDINE, in the presence of packet losses.

On optimal dynamic scheduling for sum-queue minimization in trees

We investigate the problem of minimizing the sum of the queues of all the nodes in a wireless network with a tree topology. Nodes send their packets to the trees root (sink). We consider a time-slotted system, and a primary interference model. We first consider the case where the root has only one child while the rest of the tree is arbitrary, and provide a causal sample-path delay optimal scheduling policy, i.e., at each time slot, for any traffic arrival pattern, the sum of the queues of all the nodes is minimum among all policies. We are able to fully characterize tree structures for which such policies exist. In particular, when the root has multiple children, there exists a causal sample-path delay optimal policy as long as only one child is not a leaf node. We also show that for any other tree structure there exists no causal sample-path delay optimal policy, thus underscoring the inherent limitation of using sample-path optimality as a performance metric and implying that other weaker metrics of delay performance should be investigated.

On Sample-Path Optimal Dynamic Scheduling for Sum-Queue Minimization in Trees Under the

We investigate the problem of minimizing the sum of the queue lengths of all the nodes in a wireless network with a tree topology. Nodes send their packets to the trees root (sink). We consider a time-slotted system and a K-hop interference model. We characterize the existence of causal sample-path optimal scheduling policies in these networks, i.e., we wish to find a policy such that at each time-slot, for any traffic arrival pattern, the sum of the queue lengths of all the nodes is minimum among all policies. We provide an algorithm that takes any tree and K as inputs, and outputs whether a causal sample-path optimal policy exists for this tree under the K-hop interference model. We show that when this algorithm returns FALSE, there exists a traffic arrival pattern for which no causal sample-path optimal policy exists for the given tree structure. We further show that for certain tree structures, even noncausal sample-path optimal policies do not exist. We provide causal sample-path optimal policies for those tree structures for which the algorithm returns TRUE. Thus, we completely characterize the existence of such policies for all trees under the K-hop interference model. The nonexistence of sample-path optimal policies in a large class of tree structures implies that we need to study other (relatively) weaker metrics for this problem.

K

We study a problem of detecting deterministic signals buried in correlated clutter using wireless sensor networks. We are specifically interested in developing a distributed algorithm over the network to detect the presence of a deterministic signal while keeping low communication delay and energy associated with the distributed computation. In this paper, we deploy a distributed version of the Sparse Matrix Transform (SMT) that decorrelates a signal measured by a number of sensors in order to compute a matched filter. The matched filter represents the sum of the Log-Likelihood Ratios over all the sensors of the two hypotheses corresponding to whether a deterministic signal is present or not. We show through numerical simulations that our algorithm is very efficient in terms of communication energy and delay while sustaining a high Signal-to-Clutter Ratio.

-Hop Interference Model

Motivated by the need to judiciously allocate scarce sensing resources to attain the highest benefit for the applications that sensor networks serve, in this article we develop a flexible solutions methodology for maximizing the overall reward attained, subject to constraints on the resource demands under fairly general reward or demand functions. We map a broad class of related problems for data fusion in wireless sensor networks into an integer programming problem and provide an iterative Lagrangian relaxation technique to solve it. Each iteration step involves solving for a maximum-weight independent set of an appropriately constructed graph, which, in many cases, can be obtained in polynomial time. We apply our methodology to the problem of tracking targets moving over a period of time through a nonhomogeneous, energy-constrained sensor field. With rewards represented by the quality of information attained in tracking, we study its trade-offs and relationship with energy consumption and periodic measurement taking. We finally illustrate other applications of our framework in sensor networks.

Communication efficient signal detection in correlated clutter for wireless sensor networks

We investigate the problem of minimizing the sum of the queue lengths of all the nodes in a wireless network with a tree topology. Nodes send their packets to the trees root (sink). We consider a time-slotted system, and a K-hop interference model. We characterize the existence of causal sample-path optimal scheduling policies in these networks, i.e., we wish to find a policy such that at each time slot, for any traffic arrival pattern, the sum of the queue lengths of all the nodes is minimum among all policies. We provide an algorithm that takes any tree and K as inputs, and outputs whether a causal sample-path optimal policy exists for this tree under the K-hop interference model. We show that when this algorithm returns FALSE, there exists a traffic arrival pattern for which no causal sample-path optimal policy exists for the given tree structure. We further show that for certain tree structures, even non-causal sample-path optimal policies do not exist. We provide causal sample-path optimal policies for those tree structures for which the algorithm returns TRUE. Thus, we completely characterize the existence of such policies for all trees under the K-hop interference model. The non-existence of sample-path optimal policies in a large class of tree structures implies that we need to study other (relatively) weaker metrics for this problem.