Sensor networks are fundamentally constrained b y the difficulty and energy expense of delivering information from sensors to sink. Our work has focused on garnerin g additional significant energ y improvements b y d ev isin g computationally-efficient lossless compression algorithms on the source node. These reduce the amount of data that must be passed through the network and to the sink, and thus have energy benefits that are multiplicative with the number of hops the data travels through the network. Currently, if sensor system designers want to compress acquired data, they must either develop application-specific compression algorithms or use off-the-shelf algorithms not designed for resource-constrained sensor nodes. This paper discusses the design issues involved with implementing, adapting, and customizing compression algorithms specifically geared for sensor nodes. While developing Sensor LZW (S-LZW) and some simple, but effective, variations to this algorithm, we show how different amounts of compression can lead to energy savings on both the compressing node and throughout the network and that the savings depends heavily on the radio hardware. To validate and evaluate our work, we apply it to datasets from several different real-world deployments and show that our approaches can reduce energy consumption by up to a factor of 4.5X across the network.

Data storage has become an important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. This paper introduces storage nodes to store the data collected from the sensors in their proximities. The storage nodes alleviate the heavy load of transmitting all the data to a central place for archiving and reduce the communication cost induced by the network query. This paper considers the storage node placement problem aiming to minimize the total energy cost for gathering data to the storage nodes and replying queries. We examine deterministic placement of storage nodes and present optimal algorithms based on dynamic programming. Further, we give stochastic analysis for random deployment and conduct simulation evaluation for both deterministic and random placements of storage nodes.

Abstract — We study sensor networks with energy harvesting nodes. The generated energy at a node can be stored in a buffer. A sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time at the node. For such networks we develop efficient energy management policies. First, for a single node, we obtain policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay. Next using the results for a single node, we develop efficient MAC policies.

Abstract. We compare the performance of standard data compression techniques in the presence of communication failures. Their performance is inferior to sending data without compression when the packet loss rate of a link is above 10%. We have developed fault-tolerant compression algorithms for sensor networks that are robust against packet loss and transmission delays. We show the advantage of our technique by providing results from our extensive experimental evaluation using real sensor datasets. 1

An overview of some recent advances in distributed information processing for control over wireless sensor networks is presented in this paper. Firstly, a taxonomy of fundamental control and communication schemes for these systems is introduced. Next, specific research issues are proposed and discussed with three prominent examples on distributed source coding with packet aggregation, distributed cooperative diversity and distributed cooperative localization. In regard to these examples, it is argued about some open research problems and suggestions for further investigations on joint control and communication design for distributed processing and control over wireless sensor networks.

Abstract — In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant, common decoder over a discrete time multiple access channel under various side information assumptions. In particular, we derive an achievable rate region for this communication problem.

Abstract — In this paper, we propose a theoretical setup for evaluation of energy efficiency of wireless sensor networks (WSNs) with distributed source coding (DSC) algorithms and packet aggregation (PA). We consider four topologies for DSC and three alternatives for PA, and the system model includes a realistic network architecture with multi-hop communication, automatic repeat request protocol (ARQ), and packet losses. The analysis is carried out in two steps. Firstly we derive the packet loss probability, and then evaluate the average number of packets transmitted throughout the network. This second performance index can then be mapped onto an energy efficiency indicator. The proposed model is specifically adopted for performance comparison of the different coding strategies and aggregation schemes in terms of energy efficiency. Numerical results show that packet overheads have a relevant influence on performance, while the ARQ protocol introduces negligible effects on the energy consumption. Furthermore, DSC topologies with masterslave approach and fragmentation of packets exhibit better performance.

We investigate and develop energy-efficient strategies for deployment of wireless sensor networks (WSN) for the purpose of monitoring some phenomenon of interest in a coverage region. We first describe a two-level WSN structure where the sensors in the lower level monitor their surrounding environment and the microservers in the top level provide connectivity between the sensors and a base station. We then formulate and solve the problem of assigning positions and initial energy levels to the micro-servers and concurrently partitioning the sensors into clusters assigned to individual micro-servers so as maximize the monitoring lifetime of the two-level WSN subject to a total energy budget. This problem, called MDEA, is solved for both collinear deployment and planar deployment situations. Our experimental results show that the design and deployment of such a two-level WSN increase the network lifetime by a factor of two or more compared to a flat WSN with the same total initial energy and quality of monitoring.

Abstract—We study a sensor node with an energy harvesting source. In any slot, the sensor node is in one of two modes: Wake or Sleep. The generated energy is stored in a buffer. The sensor node senses a random field and generates a packet when it is awake. These packets are stored in a queue and transmitted in the wake mode using the energy available in the energy buffer. We obtain energy management policies which minimize a linear combination of the mean queue length and the mean data loss rate. Then, we obtain two easily implementable suboptimal policies and compare their performance to that of the optimal policy. Next, we extend the Throughput Optimal policy developed in our previous work to sensors with two modes. Via this policy, we can increase the throughput and stabilize the data queue by allowing the node to sleep in some slots and to drop some generated packets. This policy requires minimal statistical knowledge of the system. We also modify this policy to decrease the switching costs. Keywords: Energy harvesting sensor nodes, Sleep-Wake Policies, Throughput Optimal Policies.

Abstract — Recently there has been an influx of work on extending a wireless sensor network’s lifetime via distributed source compression and a deployment of non-homogeneous nodes to handle the aggregation. Both of these mechanisms have been shown to increase the network’s lifetime and to normalize the energy use per unit time, but they each have requirements that might not be plausible. With distributed source compression, the distribution of the messages must be known a-priori, and the existing practical schemes tend to require modification of a layer in the network stack. Whereas with non-homogeneous node deployment the placement of the more powerful nodes is a major factor in energy savings, but many scenarios exist where this is not reasonable. We propose non-uniform entropy compression wherein bottleneck nodes do more aggressive compression, which induces synthetic-non-homogeneity in the network. Bottleneck nodes trade computation energy for transmission energy, which extends and normalizes network lifetime. Our method inserts a compression layer between Medium Access Control (MAC) and the routing layers without modification to existing network layers, thus providing a general platform for message compression. To show the effectiveness of our non-uniform entropy compression scheme, we compare five different entropy compression algorithms and correlated source coding. Our simulation validates our theory that synthetic-non-homogeneity both normalizes and reduces energy loss throughout the network. I.