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Wireless sensor networks (WSNs) have many applications and are an essential part of IoT systems. The primary functionality of a WSN is gathering data from specific points that are covered with sensor nodes and transmitting the collected data to remote units for further processing. In IoT use cases, a WSN infrastructure may need to be shared by many applications, which requires scheduling those applications to time-share the node and network resources. In this paper, we investigate the problem of application scheduling in WSN infrastructures. We focus on the scenarios where applications request a set of monitoring points to be sensed in the region a WSN spans and propose a shared-data approach utilizing multiplexed sensing of monitoring points requested by multiple applications, which reduces sensing and communication load on the network. We also propose a genetic algorithm called GABAS, and three greedy algorithms for scheduling applications onto a WSN infrastructure considering different criteria. We performed extensive simulation experiments to evaluate our algorithms and compare them to some standard scheduling methods. The results show that our proposed methods perform much better than the standard scheduling methods in terms of makespan, turnaround time, waiting time, and successful execution rate metrics. We also observed that our genetic algorithm is very effective in scheduling applications with respect to these metrics.

Many sensor node platforms used for establishing wireless sensor networks (WSNs) can support multiple radio channels for wireless communication. Therefore, rather than using a single radio channel for whole network, multiple channels can be utilized in a sensor network simultaneously to decrease overall network interference, which may help increase the aggregate network throughput and decrease packet collisions and delays. This method, however, requires appropriate schemes to be used for assigning channels to nodes for multi-channel communication in the network. Because data generated by sensor nodes are usually delivered to the sink node using routing trees, a tree-based channel assignment scheme is a natural approach for assigning channels in a WSN. We present two fast tree-based channel assignment schemes (called bottom up channel assignment and neighbor count-based channel assignment) for multi-channel WSNs. We also propose a new interference metric that is used by our algorithms in making decisions. We validated and evaluated our proposed schemes via extensive simulation experiments. Our simulation results show that our algorithms can decrease interference in a network, thereby increasing performance, and that our algorithms are good alternatives for static channel assignment in WSNs. Copyright (c) 2015 John Wiley & Sons, Ltd.

The main function of a wireless sensor network (WSN) is to gather data from a certain region and transfer the data to a center or remote locations for further processing. The collected data can be of interest for many applications. Therefore, a physical WSN owned by a single provider can be utilized by many customer applications. Additionally, the data of a particular point or sub-region can satisfy the need of multiple applications. Hence, sensing the data only once in such cases is beneficial to reduce the energy consumption, network traffic and acceptance ratio of the applications. We call this as monitoring point based shared data approach. In this paper, we focus on the placement of applications each of which requires several points to be monitored in an area a WSN covers. We first propose such a monitoring point based shared data approach for WSNs that will serve multiple dynamic applications. We also propose two methods for application placement over a shared physical WSN: one greedy method and one genetic algorithm based method called GABAP. We did extensive simulation experiments to evaluate our algorithms. The results show the effectiveness of our methods in fast and close-to-optimum placement of applications over a single network.

This paper introduces a distributed and energy-aware algorithm, called Minimum Drone Placement (MDP) algorithm, to determine the minimum number of base stations mounted on resource-rich Unmanned Aerial Vehicles (UAV-BS), commonly referred to as drone-BS, and their possible locations to provide fault tolerance with high network connectivity in heterogeneous wireless sensor networks. This heterogeneous model consists of resource-rich UAV-BSs, acting as gateways of data, as well as ordinary sensor nodes that are supposed to be connected to the UAV-BSs via multi-hop paths. Previous efforts on fault tolerance in heterogeneous wireless sensor networks attempt to determine transmission radii of the sensor nodes based on the already deployed base station positions. They assume that the base stations are stationary and arbitrarily deployed regardless of the position of the sensor nodes. Our proposed MDP algorithm takes into account the desired degree of fault tolerance and the position of ordinary sensor nodes to determine the optimal number of UAV-BSs and their locations. The MDP algorithm consists of two steps. In the first step, each sensor node chooses low-cost pairwise disjoint paths to a subset of candidate UAV-BSs, using an optimization based on the well-known set-packing problem. In the last step, depending on the desired degree of fault tolerance, MDP chooses a subset of these UAV-BS candidates using a novel optimization based on the well-known set-cover problem. Through extensive simulations, we demonstrate that the MDP achieves up to 40% improvement in UAV-connected lifetimes compared to a random and uniform distribution of UAV-BSs.

This paper illustrates the motivation, the preliminary design and implementation issues, of a new distributed simulation middleware named Advanced RTI System (ARTIS). The aim of the ARTIS middleware is to support parallel and distributed simulations of complex systems characterized by heterogeneous and distributed model components. The ARTIS design is oriented to support the model components heterogeneity, distribution and reuse, and to increase the simulation performances, scalability and speedup, in parallel and distributed simulation scenarios. Another design issue of the ARTIS framework is the dynamic adaptation of the interprocess communication layer to the heterogeneous communication support of different simulation scenarios. In this paper we illustrate the guidelines and architecture that we considered in the design and implementation of the ARTIS middleware, and we sketch some case studies that demonstrated the ARTIS utility and motivation, e.g., a distributed simulation of massively populated wireless ad hoc and sensor networks.

Embedded microcontrollers are often programmed in plain C and lacksupport for multithreading and real-time scheduling. This can makeit very cumbersome to implement multirate feedback controlapplications. We have developed the TinyRealTime kernel forthe Atmel ATmega8L AVR to show that it is feasible to usehigh-precision, deadline-based scheduling even in a tiny 8-bitprocessor with 1 KB of RAM. The kernel is demonstrated in amultirate control application, where six periodic real-time tasks(four control tasks and two PWM tasks) are used to control twoball-and-beam processes.