Neeraj Jaggi

Relay Scheduling for Cooperative Communications in Sensor Networks with Energy Harvesting

This paper considers wireless sensor networks (WSNs) with energy harvesting and cooperative communications and develops energy efficient scheduling strategies for such networks. In order to maximize the long-term utility of the network, the scheduling problem considered in this paper addresses the following question: given an estimate of the current network state, should a source transmit its data directly to the destination or use a relay to help with the transmission? We first develop an upper bound on the performance of any arbitrary scheduler. Next, the optimal scheduling problem is formulated and solved as a Markov Decision Process (MDP), assuming that complete state information about the relays is available at the source nodes. We then relax the assumption of the availability of full state information, and formulate the scheduling problem as a Partially Observable Markov Decision Process (POMDP) and show that it can be decomposed into an equivalent MDP problem. Simulation results are used to show the performance of the schedulers.

Energy-efficient data redistribution in sensor networks

We address the energy-efficient data redistribution problem in data intensive sensor networks (DISNs). The key question in sensor networks with large volumes of sensory data is how to redistribute the data efficiently under limited storage and energy constraints at the sensor nodes. The goal of the redistribution scheme is to minimize the energy consumption during the process, while guaranteeing full utilization of the distributed storage capacity in the DISNs. We formulate this problem as a minimum cost flow problem, which can be solved optimally. However, the optimal solutions centralized nature makes it unsuitable for large-scale distributed sensor networks. We thus design a distributed algorithm for the data redistribution problem which performs very close to the optimal, and compare its performance with various intuitive heuristics. Our proposed algorithm relies on potential function based computations, incurs limited message and computational overhead at both the sensor nodes and data generator nodes, and is easily implementable in a distributed manner. We analytically show the convergence of our algorithm, and demonstrate its near-optimal performance and scalability under various network scenarios considered. Finally, we implement our distributed algorithm in TinyOS and evaluate it using TOSSIM simulator, and show that it outperforms EnviroStore, the only existing scheme for data redistribution in sensor networks, in both solution quality and overhead messages.

Energy-efficient data preservation in intermittently connected sensor networks

We study data preservation in intermittently connected sensor networks, wherein the sensor nodes do not always have connected paths to the base station. In such networks, the generated data is first stored inside the network before being uploaded to the base station when uploading opportunity arises. How to preserve the data inside the network is therefore an important problem. The problem becomes more challenging when sensor nodes have finite and unreplenishable battery energy. In this paper, we identify, formulate and study the data preservation problem in the intermittently connected sensor networks under energy constraints at sensor nodes. The problem aims to preserve the data inside the network for maximum possible time, by distributing the data items from low energy nodes to high energy nodes. We first show that this problem is NP-hard. We then design a centralized greedy heuristic and a distributed data distribution algorithm, and compare their performances using simulations.

MAC layer misbehavior effectiveness and collective aggressive reaction approach

Current wireless MAC protocols are designed to provide an equal share of throughput to all nodes in the network. However, the presence of misbehaving nodes (selfish nodes which deviate from standard protocol behavior in order to get higher bandwidth) poses severe threats to the fairness aspects of MAC protocols. In this paper, we investigate various types of MAC layer misbehaviors, and evaluate their effectiveness in terms of their impact on important performance aspects including throughput, and fairness to other users. We observe that the effects of misbehavior are prominent only when the network traffic is sufficiently large and the extent of misbehavior is reasonably aggressive. In addition, we find that performance gains achieved using misbehavior exhibit diminishing returns with respect to its aggressiveness, for all types of misbehaviors considered. We identify crucial common characteristics among such misbehaviors, and employ our learning to design an effective measure to react towards such misbehaviors. Employing two of the most effective misbehaviors, we study the effect of collective aggressiveness of non-selfish nodes as a possible strategy to react towards selfish misbehavior. Particularly, we demonstrate that a collective aggressive reaction approach is able to ensure fairness in the network, however at the expense of overall network throughput degradation.

Cooperative Relay Scheduling under Partial State Information in Energy Harvesting Sensor Networks

Sensors equipped with energy harvesting and cooperative communication capabilities are a viable solution to the power limitations of Wireless Sensor Networks (WSNs) associated with current battery technology. However, the optimal scheduling of transmissions in such networks is challenging due to the requirement of complete state information of the relay nodes. This paper addresses the problem of transmission scheduling in such networks when only partial state information about the relays is available at the source. We formulate the scheduling problem as a Partially Observable Markov Decision Process (POMDP), and show that it can be decomposed into an equivalent Markov Decision Process (MDP) problem. Simulation results are used to show the performance of the scheduler.

Multi-sensor activation for temporally correlated event monitoring with renewable energy sources

Future sensor networks would comprise sensing devices with energy-harvesting capabilities from renewable energy sources, such as solar power. This paper focuses on design of efficient algorithms for multi-sensor activation to optimise overall event detection probability in presence of uncertainties in event and recharge processes. We formulate the dynamic multi-sensor activation question in a stochastic optimisation framework, and show that a time-invariant threshold policy, which maintains an appropriately chosen number of sensors active at all times, is optimal in absence of temporal correlations. Moreover, the same energy-balancing time-invariant threshold policy approaches optimality in presence of temporal correlations as well, albeit under certain limiting assumptions. We also analyse the class of correlation-dependent threshold policies and derive the range for energy-balancing thresholds. Through simulations, we compare the proposed time-invariant policy with energy-balancing correlation-dependent policies, and observe that although the latter may perform better, the performance difference is rather small in the cases studied.

Semi-Markov process based model for performance analysis of wireless LANs

In this paper, we propose a new semi-Markov process based model to compute the network parameters such as saturation throughput, for the IEEE 802.11 Distributed Coordination Function (DCF) employing the Binary Exponential Backoff (BEB). The backoff stages of BEB and their backoff intervals are modeled as the states of semi-Markov process and their state holding-times, respectively. The proposed model is simpler than Bianchis two-dimensional Markov chain based model, with the number of states in the proposed model being of the order O(m), where m is the number of backoff stages in the BEB, compared with the Bianchis model where number of states is of the order O(2m). Using the proposed semi-Markov process model, we compute the parameters of interest in wireless LANs, such as conditional collision probability, packet transmission probability, and saturation throughput. We show that the proposed model is quite accurate in computing these parameters of interest. Moreover, we show that the computation time with the proposed model is approximately one-tenth of that with Bianchis model, using Matlab simulations. Thus, the proposed model achieves accurate results with less complexity and computation time, and is suitable to be used for performance evaluation of complex protocols such as IEEE 802.11e.

An adaptive algorithm for sensor activation in renewable energy based sensor systems

Future sensor networks would comprise of sensing devices with energy harvesting capabilities from renewable energy sources such as solar power. A key research question in such sensor systems is to maximize the asymptotic event detection probability achieved in the system, in the presence of energy constraints and uncertainties. This paper focuses on the design of adaptive algorithms for sensor activation in the presence of uncertainty in the event phenomena. We borrow ideas from increase/decrease algorithms used in TCP congestion avoidance, and design an online and adaptive activation algorithm, that varies the subsequent sleep interval according to additive increase and multiplicative decrease based upon the sensors current energy level. In addition, the proposed algorithm does not depend on global system parameters, or on the degree of event correlations, and hence can easily be deployed in practical scenarios. Through extensive simulations, we demonstrate that the proposed algorithm not only achieves near-optimal performance, but also exhibits more stability with respect to sensors energy level and sleep interval variations.

Distributed Reaction Mechanisms to Prevent Selfish Misbehavior in Wireless Ad Hoc Networks

We address the issue of misbehavior detection and reaction in IEEE 802.11 based Ad Hoc networks. Selfish misbehavior involves disobeying standard protocol mechanisms to gain unfair access to the channel at the expense of other users. We outline conditions on genuine (non-misbehaving) nodes throughput to guarantee the presence of misbehavior, and propose non-adaptive and strong reaction mechanism for such aggressive misbehaviors. For selfish misbehaviors which do not result in severe throughput degradation for genuine users, we design an adaptive reaction mechanism. Both mechanisms are distributed in nature, rely only upon local information available at genuine nodes, and are thus easily implementable in practice. Proposed reaction mechanisms provide the necessary disincentive towards selfish misbehavior, and are aimed at preventing misbehavior.

On the energy efficiency of cognitive radios - A study of the Ad Hoc Wireless LAN scenario

Cognitive radios have been proposed in recent years to make more efficient use of the wireless spectrum and alleviate congestion on widely used frequency bands. A key aspect of these radios is the ‘cognition’ gained through a spectrum scanning process. The benefit of this cognition is apparent and well studied in terms of achieving better communication performance on selected spectrum. The benefits in terms of reduced energy consumption, however, due to easier channel access and less contention have not been quantified in prior work. On the other hand, spectrum scanning to gain cognition is a power-intensive process and the costs incurred in terms of energy lost need to be accounted for. Thus, it is not clear whether a cognitive radio would be more energy efficient than a conventional radio, and if so, under what circumstances. This focus on energy consumption is particularly important when considering portable communication devices that are energy constrained. This work takes a first step in this direction by exploring whether a cognitive radio can save energy over a conventional radio in the Ad Hoc Wireless LAN scenario. The interplay between different important parameters involved is analyzed and their impact on energy consumption is studied.