This paper presents a novel algorithm to rapidly create a high quality network plan for IEEE 802.11 based WLAN according to assigned design requirements. The algorithm uses a Genetic Algorithm (GA) to explore the design space, and a IEEE 802.11 rate adaptation aware QoS estimation functionality to provide feedback for the algorithm and for a network designer. The algorithm selects AP devices, locations, antennas, as well as AP configuration including transmission power and frequency channel. The algorithm was used in WLAN planning for a suburb, which is under development in Tampere-Lempaala area in Finland. Compared to manual network planning, the developed algorithm was able to create a network plan with 133 % capacity, 98 % coverage, and 93 % cost. Manually the corresponding network planning took hours, whereas the algorithm computation time was 15 minutes.

Genetic Algorithm to Optimize Node Placement and Configuration for WLAN Planning

Self-adaptive systems tend to be reactive and myopic, adapting in response to changes without anticipating what the subsequent adaptation needs will be. Adapting reactively can result in inefficiencies due to the system performing a suboptimal sequence of adaptations. Furthermore, when adaptations have latency, and take some time to produce their effect, they have to be started with sufficient lead time so that they complete by the time their effect is needed. Proactive latency-aware adaptation addresses these issues by making adaptation decisions with a look-ahead horizon and taking adaptation latency into account. In this paper we present an approach for proactive latency-aware adaptation under uncertainty that uses probabilistic model checking for adaptation decisions. The key idea is to use a formal model of the adaptive system in which the adaptation decision is left underspecified through nondeterminism, and have the model checker resolve the nondeterministic choices so that the accumulated utility over the horizon is maximized. The adaptation decision is optimal over the horizon, and takes into account the inherent uncertainty of the environment predictions needed for looking ahead. Our results show that the decision based on a look-ahead horizon, and the factoring of both tactic latency and environment uncertainty, considerably improve the effectiveness of adaptation decisions.

/pdf/proactive-self-adaptation-under-uncertainty-a-probabilistic-4jbhs9camj.pdf

Proactive self-adaptation under uncertainty: a probabilistic model checking approach

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m² over transects exceeding 1000 m². Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. This paper describes a new approach where the deployment of a combination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands. To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variability in environmental phenomena discovered by the mobile sensors and to discrete events discovered by static sensors. We begin by describing the class of important driving applications, the statistical foundations for this new approach, and task allocation. We then describe our experimental implementation of adaptive, event aware, exploration algorithms, which exploit our wireless, articulated sensors operating with deterministic motion over large areas. Results of experimental measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

Call and Response: Experiments in Sampling the Environment

Low energy consumption is a critical design requirement for most wireless sensor network (WSN) applications. Due to minimal transmission power levels, time-varying environmental factors and mobility of nodes, network neighborhood changes frequently. In these conditions, the most critical issue for energy is to minimize the transactions and time consumed for neighbor discovery operations. In this paper, we present an energy-efficient neighbor discovery protocol targeted at synchronized low duty-cycle medium access control (MAC) schemes such as IEEE 802.15.4 and S-MAC. The protocol effectively reduces the need for costly network scans by proactively distributing node schedule information in MAC protocol beacons and by using this information for establishing new communication links. Energy consumption is further reduced by optimizing the beacon transmission rate. The protocol is validated by performance analysis and experimental measurements with physical WSN prototypes. Experimental results show that the protocol can reduce node energy consumption up to 80% at 1-3m/s node mobility.

Energy-efficient neighbor discovery protocol for mobile wireless sensor networks

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data has become easier than ever. However, large scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g. pay-as-you-go infrastructure). Further, not everyone is interested in such large scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called C-MOSDEN ( Context-aware Mobile Sensor Data ENgine) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of 'selective sensing'. The computational effectiveness and efficiency of the proposed platform are investigated and is used to highlight the advantages of context-aware selective sensing.

/pdf/energy-efficient-location-and-activity-aware-on-demand-26i7m2xyfs.pdf

Energy Efficient Location and Activity-aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds

Wireless sensor network (WSN) application development is not an easy task due to its resource constrained nature and vast feature rich application space. Several abstractions are harnessed to ease out the difficult WSN application development. In this paper, three levels of abstractions are classified from the existing literature: node, network, and infrastructure abstractions. Since the node and network abstractions are already a well-studied area, the infrastructure abstraction is surveyed in detail to complete knowledge. Technology interoperability, service discovery, metadata support, and processing support are found as basic requirements for infrastructure abstraction. Problematic security and quality of service topics are discussed and the open research questions of ontology, service discovery, distributed processing, and performance metrics are defined. Finally, a distributed middleware design is presented as a possible solution for the key open research question: how to utilize capabilities of the abstracted technologies.

A Survey of Wireless Sensor Network Abstraction for Application Development

This paper presents the design, implementation, and practical real world experiments of an energy optimized multi-hop wireless sensor network (WSN) targeted at environmental monitoring. The WSN is fully autonomous and consists of energy-efficient and scalable communication protocols and low-power hardware platform. Software tools are developed for configuring and analyzing large scale networks. The network has been deployed in outdoor environment consisting of 20 nodes covering over 2 km2 area. The results show that the multi-hop network works autonomously, reacts to environmental changes, and is able to operate temperatures down to -30 °C. The hardware nodes operating on 433 MHz frequency provide over 1 km communication distances, while still having sufficient throughput and low energy consumption. The deployed nodes had a lifetime of 6 months with a 1600 mAh battery, while generating 4 packets per minute.

Design, implementation, and experiments on outdoor deployment of wireless sensor network for environmental monitoring

Evaluation of throughput estimation models and algorithms for WLAN frequency planning

Internet-of-Things (IoT) consists of interconnected heterogeneous devices that ubiquitously interact with physical world. The devices are often resource constrained in terms of energy, computation, and communication resources. Distributing processing between these heterogeneous devices could yield to better performance and sharing, and extending resources of the devices could yield to more intelligent ubiquitous applications. Such a design can be called as “embedded cloud”, which is defined in this paper. An embedded cloud design is presented that consists of distributable Process Description Language (PDL), Distributed Middleware (DiMiWa), and an infrastructure. As a result, PDL can execute distributed processes and share resources as services over heterogeneous IoT devices with help of DiMiWa and the infrastructure. The design is evaluated with a prototype implementation, where PDL and DiMiWa are executed on a small 8-bit microcontroller-based IoT device. The implementation requires only 5122 B of program memory (4% of the available), consumes under 1 ms of CPU time per process in the worst case, and allows over 100 simultaneous services per device.

https://journals.sagepub.com/doi/pdf/10.1155/2013/790130

An Embedded Cloud Design for Internet-of-Things:

Frequency optimization is required to maximize the WLAN throughput in environments where several networks coexist. This paper evaluates four throughput estimation models and two optimization algorithms. Throughput is selected as the optimization criteria for channel assignment. Thus, the result of a throughput estimation model is used as an input for an optimization algorithm. The target is a frequency plan that maximizes multi-cell WLAN throughput. The throughput estimation models are based on radio spectrum usage, practical throughput measurements, WLAN protocol behavior, and theoretical coverage estimations. The models use separate functions for defining the minimum channel distance. In the evaluation, Genetic Algorithm (GA) and a distributed optimization algorithm produce the final frequency plan. A dedicated simulator has been implemented for the evaluation. As overall results, the evaluated throughput estimation models did not produce significant WLAN throughput improvements compared to each other. Still, the selection of the throughput estimation model and optimization algorithm pair is significant for results, since certain combinations cause poor results.

Marko Hännikäinen

Papers

A Survey of Wireless Sensor Network Abstraction for Application Development

Design, implementation, and experiments on outdoor deployment of wireless sensor network for environmental monitoring

Evaluation of throughput estimation models and algorithms for WLAN frequency planning

An Embedded Cloud Design for Internet-of-Things:

Evaluation of throughput estimation models and algorithms for WLAN frequency planning