Optimal data compression for lifetime maximization in wireless sensor networks operating in stealth mode

Abstract

Contextual privacy in Wireless Sensor Networks (WSNs) is concerned with protecting contextual information such as whether, when, and where the data is collected. In this context, hiding the existence of a WSN from adversaries is a desirable feature. One way to mitigate the sensor nodes' detectability is by limiting the transmission power of the nodes (i.e., the network is operating in the stealth mode) so that adversaries cannot detect the existence of the WSN unless they are within the sensing range of the WSN. Position dependent transmission power adjustment enables the network to maintain its level of stealth while allowing nodes farther from the network boundary to use higher transmission power levels. To mitigate the uneven energy dissipation characteristic, nodes that cannot dissipate their energies on communications reduce the amount of data they generate through computation so that the relay nodes convey less data. Dynamic data compression/decompression strategies reduce the amount of data to be communicated, thus, they achieve better energy savings when compared to static compression/decompression of data in which the data is always compressed independently of the power transmission strategy. In this study, we investigate various data compression strategies to maximize the lifetime of WSNs employing contextual privacy measures through a novel Mathematical Programming framework.

Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

“…Because of computation complexity of solving the optimization, the authors proposed a heuristic approach, which enables the nodes farther away from the base station to always compress their data and those closer to the base station not to compress data. Incebacak et al 18 have considered the data compression in a stealth mode of WSNs, where each sensor node has a different limited transmission power depending on its position in order not to be detected from adversaries. They proposed an optimization framework that jointly considers the network privacy preservation and the multi-level compression presented in Tavli et al 17 in order to increase the network lifetime while retaining the network privacy.…”

“…[17][18][19] In Tavli et al, 17 an optimal data compression scheme was formulated as an optimization problem that maximizes the minimum lifetime of the sensor nodes in order to increase the network lifetime. It is assumed that each node can compress and decompress raw data with multiple compression levels.…”

“…[12][13][14][15][16][17][18][19] Using an appropriate compression method for sensing data, sensor nodes can transmit and receive smaller-size data packets. As a result, the energy consumption for data transmission at sensor nodes can be significantly reduced.…”

An Optimized WSN Design for Latency-Critical Smart Grid Applications

“…Based on (2), (9), and (10) our aim is to solve two important optimization problems for each member of a WSN cluster: …”

“…The early phases of design, development, and deployment of WSNs focused on conserving the power resources of the sensor nodes (see [3,5] for details). There are a number of studies that consider optimizing the power consumption of WSNs or optimizing certain parameters of the 802.15.4 standard, under power consumption constraints [6][7][8][9]. With the development of high efficiency energy harvesting mechanisms, sensor nodes can harvest enough power to continue with their operation.…”