Formalization of Protocol Engineering Concepts
IEEE Transactions on Computers - Special issue on protocol engineering
Wireless integrated network sensors
Communications of the ACM
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Protocol Design: Redefining the State of the Art
IEEE Software
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
A Survey of Fault Management in Wireless Sensor Networks
Journal of Network and Systems Management
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
gMAP: efficient construction of global maps for mobility-assisted wireless sensor networks
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
A self-management framework for wireless sensor networks
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
Energy-efficient design of wireless sensor networks based on finite energy budget
Computer Communications
Lifetime Maximization in Wireless Sensor Networks
International Journal of Wireless Networks and Broadband Technologies
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The key challenge in the design of a wireless sensor network is maximizing its lifetime. This is a fundamental problem and new protocol engineering principles need to be established in order to achieve this goal. The information about the amount of available energy in each part of the network is called the energy map and can be useful to increase the lifetime of the network. In this paper, we propose using the energy map as a protocol engineering principle for this kind of network. We argue that an energy map can be the basis for the entire design trajectory including all functionalities to be included in a wireless sensor network. Furthermore, we show how to construct an energy map using both probabilistic and statistical prediction-based approaches. Simulation results compare the performance of these approaches with a naive one in which no prediction is used. The experiments performed use an energy dissipation model that we have proposed to simulate the behavior of a sensor node in terms of energy consumption. The results show that prediction-based approaches outperform the naive in a variety of parameters.