Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Data Transmission with Reduced Delay for Distributed Acoustic Sensors

This paper proposes a channel access control scheme fit to dense acoustic sensor nodes in a sensor network. In the considered scenario, multiple acoustic sensor nodes within communication range of a cluster head are grouped into clusters. Acoustic sensor nodes in a cluster detect acoustic signals and convert them into electric signals (packets). Detection by acoustic sensors can be executed periodically or randomly and random detection by acoustic sensors is event driven. As a result, each acoustic sensor generates their packets (50bytes each) periodically or randomly over short time intervals (400ms~4seconds) and transmits directly to a cluster head (coordinator node). Our approach proposes to use a slotted carrier sense multiple access. All acoustic sensor nodes in a cluster are allocated to time slots and the number of allocated sensor nodes to each time slot is uniform. All sensor nodes allocated to a time slot listen for packet transmission from the beginning of the time slot for a duration proportional to their priority. The first node that detect the channel to be free for its whole window is allowed to transmit. The order of packet transmissions with the acoustic sensor nodes in the time slot is autonomously adjusted according to the history of packet transmissions in the time slot. In simulations, performances of the proposed scheme are demonstrated by the comparisons with other low rate wireless channel access schemes.Comment: Accepted to IJDSN, final preprinted versio

On Optimal Neighbor Discovery

Mobile devices apply neighbor discovery (ND) protocols to wirelessly initiate a first contact within the shortest possible amount of time and with minimal energy consumption. For this purpose, over the last decade, a vast number of ND protocols have been proposed, which have progressively reduced the relation between the time within which discovery is guaranteed and the energy consumption. In spite of the simplicity of the problem statement, even after more than 10 years of research on this specific topic, new solutions are still proposed even today. Despite the large number of known ND protocols, given an energy budget, what is the best achievable latency still remains unclear. This paper addresses this question and for the first time presents safe and tight, duty-cycle-dependent bounds on the worst-case discovery latency that no ND protocol can beat. Surprisingly, several existing protocols are indeed optimal, which has not been known until now. We conclude that there is no further potential to improve the relation between latency and duty-cycle, but future ND protocols can improve their robustness against beacon collisions.Comment: Conference of the ACM Special Interest Group on Data Communication (ACM SIGCOMM), 201

Implementation of RTOS to the WSN node

Bezdrátové senzorické sieťe zväčša používajú `event-driven` operačné systémy. Táto práca diskutuje výhody nevýhody použitia RTOS v bezdrátových senzorických sieťach. Najvhodnejší RTOS je vybratý a sú podniknuté všetky kroky aby bolo možne demonštrovať schopnosť mikrokontrolérov Gecko od EnergyMicro prevádzkovať tento RTOS s nízkou spotrebou energie a demonštrovať jednoduchú bezdrátovú komunikáciu s Atmel AT86RF212 rádiami.Wireless sensors networks mostly use event-driven OSes. This works discusses pros and cons of using RTOS in wirless sensors networks. A most appropriate RTOS is chosen and all necessary steps are undergone to demonstrate EnergyMicro Gecko MCU's ability to run the RTOS with low energy consumption and demonstrate wireless simple communication with Atmel AT86RF212 radios.

Topology Construction in RPL Networks over Beacon-Enabled 802.15.4

In this paper, we propose a new scheme that allows coupling beacon-enabled IEEE 802.15.4 with the RPL routing protocol while keeping full compliance with both standards. We provide a means for RPL to pass the routing information to Layer 2 before the 802.15.4 topology is created by encapsulating RPL DIO messages in beacon frames. The scheme takes advantage of 802.15.4 command frames to solicit RPL DIO messages. The effect of the command frames is to reset the Trickle timer that governs sending DIO messages. We provide a detailed analysis of the overhead incurred by the proposed scheme to understand topology construction costs. We have evaluated the scheme using Contiki and the instruction-level Cooja simulator and compared our results against the most common scheme used for dissemination of the upper-layer information in beacon-enabled PANs. The results show energy savings during the topology construction phase and in the steady state

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Synchronization service integrated into routing layer in wireless sensor networks

The time synchronization problem needs to be considered in a distributed system. In Wireless Sensor Networks (WSNs) this issue must be solved with limited computational, communication and energy resources. Many synchronization protocols exist for WSNs. However, in most cases these protocols are independent entities with specific packets, communication scheme and network hierarchy. This solution is not energy efficient. Because it is very rare for synchronization not to be necessary in WSNs, we advocate integrating the synchronization service into the routing layer. We have implemented this approach in a new synchronization protocol called Routing Integrated Synchronization Service (RISS). Our tests show that RISS is very time and energy efficient and also is characterized by a small overhead. We have compared its performance experimentally to that of the FTSP synchronization protocol and it has proved to offer better time precision than the latter protocol