Peripheral Routing Protocol – a new routing protocol proposal for a realistic WSN mobility model

Wireless sensor networks (WSNs) are changing our way of life just as the internet has revolutionized the way people communicate with each other. Future wireless networks are envisioned to be robust, have simple and efficient communication between nodes and self-organizing dynamic capabilities. When new nodes join in, a self-configuring network has to have the ability to include these nodes in its structure in real time, without human or machine interference. The need for a destination node (D) which moves at the periphery of wireless sensor networks can be argued from different points of view: the first is that different WSN scenarios require data gathering in such a way; the second point is that this type of node movement maximizes network lifetime because it offers path diversity preventing the case where the same routes are used excessively. However the peripheral movement model of the mobile destination does not resemble any mobility models presented in the WSN literature. In this thesis a new realistic WSN sink mobility model entitled the “Marginal Mobility Model” (MMM) is proposed. This was introduced for the case when the dynamic destination (D), moving at the periphery, frequently exits and enters the WSN coverage area. We proved through Qualnet simulations that current routing protocols recommended for Mobile Ad Hoc Networks (MANETs) do not support this sink mobility model. Because of this, a new routing protocol is proposed to support it called the Peripheral Routing Protocol (PRP). It will be proven through MATLAB simulations that, for a military application scenario where D’s connectivity to the WSN varies between 10%-95%, compared with the 100% case, PRP outperforms routing protocols recommended for MANETs in terms of throughput (T), average end to end delay (AETED) and energy per transmitted packet (E). Also a comparison will be made between PRP and Location-Aided Routing (LAR) performance when D follows the MMM. Analytical models for both PRP and LAR are proposed for T and E. It is proved through MATLAB simulations that, when compared with LAR, PRP obtains better results for the following scenarios: when the WSN size in length and width is increased to 8000 m and one packet is on the fly between sender and sink, PRP sends 103% more data and uses 84% less energy; when more data packets are on the fly between sender and sink, PRP sends with 99.6% more data packets and uses 81% less energy; when the WSN density is increased to 10,000 nodes PRP uses 97.5% less energy; when D’s speed in increased to 50 Km/h, PRP sends 74.7% more data packets and uses 88.4% less energy

Algorithms for Energy Efficiency in Wireless Sensor Networks

The recent advances in microsensor and semiconductor technology have opened a new field within computer science: the networking of small-sized sensors which are capable of sensing, processing, and communicating. Such wireless sensor networks offer new applications in the areas of habitat and environment monitoring, disaster control and operation, military and intelligence control, object tracking, video surveillance, traffic control, as well as in health care and home automation. It is likely that the deployed sensors will be battery-powered, which will limit the energy capacity significantly. Thus, energy efficiency becomes one of the main challenges that need to be taken into account, and the design of energy-efficient algorithms is a major contribution of this thesis. As the wireless communication in the network is one of the main energy consumers, we first consider in detail the characteristics of wireless communication. By using the embedded sensor board (ESB) platform recently developed by the Free University of Berlin, we analyze the means of forward error correction and propose an appropriate resync mechanism, which improves the communication between two ESB nodes substantially. Afterwards, we focus on the forwarding of data packets through the network. We present the algorithms energy-efficient forwarding (EEF), lifetime-efficient forwarding (LEF), and energy-efficient aggregation forwarding (EEAF). While EEF is designed to maximize the number of data bytes delivered per energy unit, LEF additionally takes into account the residual energy of forwarding nodes. In so doing, LEF further prolongs the lifetime of the network. Energy savings due to data aggregation and in-network processing are exploited by EEAF. Besides single-link forwarding, in which data packets are sent to only one forwarding node, we also study the impact of multi-link forwarding, which exploits the broadcast characteristics of the wireless medium by sending packets to several (potential) forwarding nodes. By actively selecting a forwarder among all nodes that received a packet successfully, retransmissions can often be avoided. In the majority of cases, multi-link forwarding is thus more efficient and able to save energy. In the last part of this thesis, we present a topology and energy control algorithm (TECA) to turn off the nodes' radio transceivers completely in order to avoid idle listening. By means of TECA, a connected backbone of active nodes is established, while all other nodes may sleep and save energy by turning off their radios. All algorithms presented in this thesis have been fully analyzed, simulated, and implemented on the ESB platform. They are suitable for several applications scenarios and can easily be adapted even to other wireless sensor platforms

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects

ITIKI: Bridge between African indigenous knowledge and modern science on drought prediction

The now more rampant and severe droughts have become synonymous with Sub-Saharan Africa; they are a major contributor to the acute food insecurity in the Region. Though this scenario may be replicated in other regions in the globe, the uniqueness of the problem in Sub-Saharan Africa is to be found in the ineffectiveness of the drought monitoring and predicting tools in use in these countries. Here, resource-challenged National Meteorological Services are tasked with drought monitoring responsibility. The main form of forecasts is the Seasonal Climate Forecasts whose utilisation by small-scale farmers is below par; they instead consult their Indigenous Knowledge Forecasts. This is partly because the earlier are too supply-driven, too ""coarse"" to have meaning at the local level and their dissemination channels are ineffective. Indigenous Knowledge Forecasts are under serious threat from events such as climate variations and ""modernisation""; blending it with the scientific forecasts can mitigate some of this. Conversely, incorporating Indigenous Knowledge Forecasts into the Seasonal Climate Forecasts will improve its relevance (cultural and local) and acceptability, hence boosting its utilisation among small-scale farmers. The advantages of such a mutual symbiosis relationship between these two forecasting systems can be accelerated using ICTs. This is the thrust of this research: a novel drought-monitoring and predicting solution that is designed to work within the unique context of small-scale farmers in Sub-Saharan Africa. The research started off by designing a novel integration framework that creates the much-needed bridge (itiki) between Indigenous Knowledge Forecasts and Seasonal Climate Forecasts. The Framework was then converted into a sustainable, relevant and acceptable Drought Early Warning System prototype that uses mobile phones as input/output devices and wireless sensor-based weather meters to complement the weather stations. This was then deployed in Mbeere and Bunyore regions in Kenya. The complexity of the resulting system was enormous and to ensure that these myriad parts worked together, artificial intelligence technologies were employed: artificial neural networks to develop forecast models with accuracies of 70% to 98% for lead-times of 1 day to 4 years; fuzzy logic to store and manipulate the holistic indigenous knowledge; and intelligent agents for linking the prototype modules

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp