The remarkable advances in sensing and communication technologies have introduced increasingly low-cost, smart and portable sensors that can be embedded everywhere and play an important role in environmental sensing applications such as air quality monitoring. These user-friendly wireless sensor platforms enable assessment of human exposure to air pollution through observations at high spatial resolution in near-realtime, thus providing new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. However, data quality from such platforms is a concern since sensing hardware of such devices is generally characterized by a reduced accuracy, precision, and reliability. Achieving good data quality and maintaining error free measurements during the whole system lifetime is challenging. Over time, sensors become subject to several sources of unknown and uncontrollable faulty data which comprise the accuracy of the measurements and yield observations far from the expected values. This paper investigates calibration of low-cost air quality sensors in a real sensor network deployment. The approach leverages on the availability of sensor arrays in a wireless node to estimate parameters that minimize the calibration error using fusion of data from multiple sensors. The obtained results were encouraging and show the effectiveness of the approach compared to a single sensor calibration.

A constrained relay placement problem satisfying application requirements in terms of network lifetime and end-to-end (e2e) delay in Wireless Sensor Networks (WSN) is investigated in this paper. The network and the traffic are adequately modeled considering uniform node deployment and low data rate periodic traffic generation. An optimization problem is defined to obtain the minimum number of relays to be deployed, at each level of the network, in order to fulfil network duty-cycle and e2e delay constraints under anycast forwarding based on the wake-up period parameter of the duty-cycle MAC protocol. Since the optimization problem is non-convex, an alternative and efficient algorithm for relay node placement called EDC-RP (Energy-Delay Constrained Relay Placement) is introduced. The comparison of the proposed node deployment strategy with state-of-the-art relay placement methods demonstrates the gain of the heuristic in terms of deployment cost (number of relays) over other solutions while fulfilling the application constraints.

Optimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multiobjective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol-specific MAC parameters. From the optimization framework, a game is first defined by the Nash bargaining solution (NBS) to assure energy consumption and e2e delay balancing. Secondy, the Kalai-Smorodinsky bargaining solution (KSBS) is used to find an equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements (i.e., the maximum latency tolerated by the application and the initial energy budget of nodes), the proposed framework allows to set tunable system parameters to reach a fair equilibrium point that dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art wireless sensor network (WSN) MAC protocols: B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The article shows the effectiveness and scalability of such a framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements.

Optimizing energy consumption and end-to-end (e2e) packet delay in energy constrained distributed wireless networks is a conflicting multi-objective optimization problem. This paper investigates this trade-off from a game-theoretic perspective, where the two optimization objectives are considered as virtual game players that attempt to optimize their utility values. The cost model of each player is mapped through a generalized optimization framework onto protocol specific MAC parameters. A cooperative game is then defined, in which the Nash Bargaining solution assures the balance between energy consumption and e2e packet delay. For illustration, this formulation is applied to three state-of-the-art wireless sensor network MAC protocols; X-MAC, DMAC, and LMAC as representatives of preamble sampling, slotted contention-based, and frame-based MAC categories, respectively. The paper shows the effectiveness of such framework in optimizing protocol parameters for achieving a fair energy-delay performance trade-off, under the application requirements in terms of initial energy budget and maximum e2e packet delay. The proposed framework is scalable with the increase in the number of nodes, as the players represent the optimization metrics instead of nodes.

The real-world node deployment aspect is investigated, while considering cost minimization for resolving the energy hole around the sink, which represents a serious problem in typical sensor networks with uniform distribution. A novel strategy is proposed that is based on the use of two sinks and a few extra relay nodes close to the sinks' areas. The traffic is then alternatively sent to the sinks in every other cycle. As a second contribution, an efficient data collection mechanism has been developed to determine the optimal data rate that meets delay requirements of individual sensor reports and improves the network lifetime. The comparison of the proposed node deployment strategy with uniform, non-uniform geometric and linear increase node distributions demonstrates that the cost of the proposed solution is very close to that of the uniform distribution and much lower than all the others, while achieving a load balancing at the same order of the state-of-the-art solutions.

We present Duo-MAC, an asynchronous cascading wake-up scheduled MAC protocol for heterogeneous traffic forwarding in low-power wireless networks. Duo-MAC deals with energy-delay minimization problem and copes with transmission latency encountered by Today's duty-cycled protocols when forwarding heterogeneous traffic types. It switches, according to the energy and delay requirements, between Low Duty cycle (LDC) and High Duty Cycle (HDC) operating modes, and it quietly adjusts the wake-up schedule of a node according to (i) its parent's wake-up time and (ii) its estimated load, using an effective real-time signal processing linear traffic estimator. As a second contribution, Duo-MAC, proposes a service differentiation through an improved contention window adaptation algorithm to meet delay requirements of heterogeneous traffic classes. Duo-MAC's efficiency stems from balancing between the two traffic award operation modes. Implementation and experimentation of Duo-MAC on a MicaZ mote platform reveals that the protocol outperforms other state-of-the-art MAC protocols from the energy-delay minimization perspective.

This paper describes the implementation and evaluation of R4Syn protocol on MICAz platform and TinyOS operating system. The contribution is two folds. First, the implementation uses thorough maximum-likelihood estimators (MLE) in the joint offset/skew model, while all similar MLEbased estimators are merely evaluated with theoretical and numerical analysis thus far, and empirical solutions use simple computation estimators, such as offset-only models, or linear regression for skew estimation. Difficulties that has been encountered and overcome are reported in this paper. The second contribution is to consider fault-tolerance, an aspect that has been completely abstracted in previous works. The implementation assures correct behavior despite nodes failure or packet loss, as demonstrated by the experiments. Experimental results also demonstrate microsecond-level precision and long-term validity of the estimators in the joint skew/offset model.

This paper considers slotted duty-cycled medium access control (MAC) protocols, where sensor nodes periodically and synchronously alternate their operations between active and sleep modes to save energy. Communications can occur only when nodes are in active mode. The synchronous feature makes these protocols more appropriate for delay-sensitive applications than asynchronous protocols. With asynchronous protocols, additional delay is needed for the sender to meet the receiver's active period. This is eliminated with synchronous approaches, where nodes sleep and wake up all together. Moreover, the contention-based feature makes the protocols - considered in this paper - conceptually distributed and more dynamic compared to TDMA protocols. Duty cycling allows obtaining significant energy saving vs. full duty cycle (sleepless) protocols. However, it may result in significant latency. Forwarding a packet over multiple hops often requires multiple operational cycles (sleep latency), i.e. nodes have to wait for the next cycle to forward data at each hop. Timeliness issues of slotted contention-based MAC protocols are dealt with in this paper, where a comprehensive review and taxonomy is provided. The main contribution is to study and classify the protocols from the delay-efficiency perspective.