Minimizing Energy Consumption in Body Sensor Networks via Convex Optimization

Abstract

Body Sensor Networks (BSNs) consist of miniature sensors deployed on or implanted into the human body for health monitoring. Conserving the energy of these sensors, while guaranteeing a required level of performance, is a key challenge in BSNs. In terms of communication protocols, this translates to minimizing energy consumption while limiting the latency in data transfer. In this paper, we focus on polling-based communication protocols for BSNs, and address the problem of optimizing the polling schedule to achieve minimal energy consumption and latency. We show that this problem can be posed as a geometric program, which belongs to the class of convex optimization problems, solvable in polynomial time. We also introduce a dynamic priority vector for each sensor, based on the observation that relative priorities of sensors in a BSN change over time. This vector is used to develop a decision-tree based approach for resolving scheduling conflicts among devices. The proposed framework is applicable to a broad class of periodic polling-based communication protocols. We design one such protocol in detail and show that it achieves an improvement of approximately 45% over the widely accepted standard IEEE 802.15.4 MAC protocol.

Research directions in energy-sustainable cyber–physical systems

“…Further, as mentioned previously, since periodic workload (where the arrival and execution of workloads are repeated) are inherently offline in nature there is no classification made for online algorithms supporting such workload. An example algorithm for LPF class supporting periodic offline workload is a MAC protocol for BSNs [37]. An important aspect in designing offline algorithms for aperiodic workload is the higher complexity because of the higher knowledge of the workload.…”

Knowledge-based design space exploration of wireless sensor networks

“…The protocol is quite common, thus the knowledge domain defined in this Section can be useful for future works. The results presented here are based on the models presented in [12] 15.4, the coordinator is the head of the network and determines the structure of the communication. In the standard, the communication is divided into sequential frames delimited by specific packets called beacons (Figure 4).…”

“…When an analytical description of the WSN (or, at least, of a specific part of it) is available, the optimization can be performed using ad hoc heuristic algorithms (e.g., in [8] to solve the network connectivity problem) or efficient techniques such as convex optimization (e.g., [15] for energy/delay optimization). The simulation-based estimation, on the other hand, has a black-box nature that does not allow any analytical consideration during the execution of the algorithm.…”

“…For each state in s, all possible actions are applied, generating the configurations that differ from s0 by one parameter (lines [13][14][15][16][17][18][19][20][21][22][23][24][25][26]. For all the generated configurations (obtained by applying a in si), s k metrics are partitioned, D and T P (si, a, s k ) are updated.…”

Design exploration of energy-performance trade-offs for wireless sensor networks

“…The right tradeoff between these two objectives, as well as the prevention of undesired behaviors such as unbalanced performance among the different nodes of the WSN, can be guaranteed by accurately evaluating the network configurations during the design phase. In order to help the designer during the energy-performance tradeoff analysis, many design space exploration (DSE) techniques for WSNs have been proposed in the literature [3] [4], and most of the classic optimization algorithms can also be adapted to WSNs with a low effort. However, providing such algorithms with an accurate system-level estimation of the WSN performance is still an open problem, and it is necessary to correctly lead the DSE algorithm to the detection of the Pareto-optimal network configurations.…”