In recent years, solar power system installations are rapidly increased in domestic buildings. The Photovoltaic (PV) electricity produced in these systems is often underutilized due to the lack of adaptability to variations in load demand of the building appliances. The use of a building energy management and automation system (BEMAS) will make it possible to increase the consumption of PV electricity and to reduce the consumption of grid electricity. This paper presents a comprehensive BEMAS solution that autonomously controls the various parameters of solar inverters to reduce the under-utilization of PV power. The forecasting of PV power and load is carried out and then, by using a number of algorithms, human-centric demand-side management and efficient battery charging using solar inverters are performed. The major benefit achieved is the reduction of electricity cost by improving the contribution of PV power in grid-tied solar power systems.

In this paper, we propose a new approach to implementing hierarchical graph neuron (HGN), an architecture for memorizing patterns of generic sensor stimuli, through the use of vector symbolic architectures. The adoption of a vector symbolic representation ensures a single-layer design while retaining the existing performance characteristics of HGN. This approach significantly improves the noise resistance of the HGN architecture, and enables a linear (with respect to the number of stored entries) time search for an arbitrary subpattern.

We propose a new method, called 3-D image warping-based depth video compression (IW-DVC), for fast and efficient compression of depth images captured by mobile RGB-D sensors. The emergence of low-cost RGB-D sensors has created opportunities to find new solutions for a number of computer vision and networked robotics problems, such as 3-D map building, immersive telepresence, or remote sensing. However, efficient transmission and storage of depth data still presents a challenging task to the research community in these applications. Image/video compression has been comprehensively studied and several methods have already been developed. However, these methods result in unacceptably suboptimal outcomes when applied to the depth images. We have designed the IW-DVC method to exploit the special properties of the depth data to achieve a high compression ratio while preserving the quality of the captured depth images. Our solution combines the egomotion estimation and 3-D image warping techniques and includes a lossless coding scheme that is capable of adapting to depth data with a high dynamic range. IW-DVC operates at a high speed, suitable for real-time applications, and is able to attain an enhanced motion compensation accuracy compared with the conventional approaches. In addition, it removes the existing redundant information between the depth frames to further increase compression efficiency. Our experiments show that IW-DVC attains a very high performance yielding significant compression ratios without sacrificing image quality.

We consider the self-calibration problem (estimation of location and orientation of multiple camera sensors), in visual sensor networks equipped with RGB-D cameras. We propose two algorithms based on feature matching and relative pose estimation. First one uses Floyd-Warshall algorithm, and can accurately estimate the camera locations. On the other hand, the second algorithm is more scalable than the first one, and can be used in large networks if high accuracy is not an issue. Numerical results demonstrate that both algorithms, depending on the network topology, can be used for self-calibration in RGB-D equipped visual sensor networks.

Efficient fault detection and characterization are crucial requirements for automated network diagnosis systems. In this paper, we present normalized statistical signatures (NSSs), a network ‘soft-failure’ characterization technique for user devices (UDs) based upon exhaustive sets of aggregated statistical features extracted from transmission control protocol (TCP) packet streams. TCP streams are collected on-demand (e.g. upon user complaint) with fixed data transfer limits to capture the artifacts resulting from UD faults. NSSs created using live network and testbed data are able to uniquely characterize many faults and offer insight into how various types of features are affected by the faults and networks. We then introduce the link adaptive signature estimation (LASE) technique to reduce the quantities of collected NSSs required for generalized diagnostic systems with variable link parameters. We create feature estimator functions using multivariate regression techniques to generate artificial NSSs, which are subsequently used to train machine learning systems that have robust generalization capabilities. Performance of a prototype fault classifier system based on NSSs shows that an overall detection accuracy of 98% can be achieved for eight types of faults in a live network environment. In this paper, we specifically focus on formulating the basic framework of NSS and LASE, and limit the analysis to wired networks. This work can later be extended to encompass more complex fixed and mobile wireless networking environments. We expect that the combination of NSSs and LASE can serve as the foundation of next-generation automated network diagnosis systems.

We present an automated solution for rapid diagnosis of both known and unknown “soft-failures” in network User Devices (UDs). A multiclass classifier is first trained with the known faults and during diagnosis, the unknown faults are clustered to determine the existence of a new fault. Then, in an iterative process, the classifier is re-trained with the newly detected fault. The system relies on 410 features long Normalized Statistical Signature (NSSs) for fault characterization. Since, the high dimensionality of the NSS can create model overfitting, we propose EigenNSS, a transformed signature with lower dimensions and minimum information loss. The system is evaluated with live network data of 17 emulated UD faults. The results show an overall detection accuracy of 97.2%with minimum false positives and dimensionality reduction of 93.9%. Also, compared with the NSS, the EigenNSS has faster training and diagnosis times suitable for on-demand as well as real-time diagnostic applications.

This thesis presents a vertical handover decision (VHD) scheme for optimizing the efficiency of vertical handover processes in the Fourth Generation (4G) heterogeneous wireless networks. The scheme consists of three closely integrated modules: Handover necessity estimation, handover target selection, and handover triggering condition estimation. Handover necessity estimation module determines whether a handover is necessary to an available network. Handover target selection module chooses the ``best'' network among the available candidates based on a set of criteria. Finally, handover triggering condition estimation module determines the right moment to initiate a handover out of the currently connected network. 4G wireless networks are expected to support mechanisms for tight integration and cooperation of divergent access network technologies. In such networks of heterogeneous nature, roaming users will experience frequent handovers across network boundaries. Thus, to ensure seamless roaming and efficient resource usage over dissimilar networks, intelligent VHD algorithms need to be used extensively. The research project presented in this thesis report focuses on this problem and provides an optimized VHD scheme, which minimizes the handover failures, unnecessary handovers and connection breakdowns whilst maintaining users' satisfaction at high levels. In addition, the scheme also provides mechanisms for mobile applications to control the tradeoff between the usage of the preferred access network and number of handovers or connection breakdowns. Simulation based performance evaluations demonstrate that the scheme reduces the number of handover failures, unnecessary handovers and connection breakdowns by up to 80%, 70% and 70%, respectively. They also show an increase of up to 50% in the satisfaction level of users.

Wireless access in vehicular environment (WAVE) is a technology developed for high data rate vehicle-to-vehicle and vehicle-to-infrastructure communications. Accurate estimation of speed of a vehicular node is essential in applications of WAVE such as collision avoidance systems and intersection safety applications. A novel speed estimation technique in WAVE is presented in this paper. The method estimates the relative speed of a vehicle using the Doppler shift which causes frequency offset in the received orthogonal frequency division multiplexed subcarriers. The frequency offset in the received subcarriers is calculated using a cross correlation metric between the transmitted pilot subcarriers and the received pilot subcarriers. The method gives good estimates of relative speed even at low signal to noise ratios and the simulations demonstrate that wide range of speeds (0 to 360 km/h) can be accurately estimated.

In near future, various wireless access technologies will be used together to provide seamless and cost effective connectivity to mobile users. Multiple wireless access systems with overlapping coverage fields will be accessible to a mobile terminal with multiple interfaces. One problem arising from the availability of alternatives is the unnecessary occurrences of handovers (especially vertical ones) that lead to excessive resource consumption. To avoid this, a suite of algorithms need to be deployed to limit the number of handovers. In this paper, we present an algorithm which is run in the mobile terminal and minimizes the number of handovers when the device detects the presence of wireless local area network (WLAN) signals. Conventionally, a mobile terminal should switch from cellular to WLAN whenever WLAN connectivity is available, to make use of the WLAN's high speed and low cost as much as possible. However, a considerable proportion of these handovers will be detrimental to the call quality due to potential failures. Our algorithm reduces the number of handovers whenever the predicted traveling distance inside the WLAN cell is smaller than a threshold value. Through performance analysis we show that our algorithm successfully minimizes the probability of unnecessary handovers, and through simulation we discuss about the accuracy of the distance prediction based on the received signal strength measurements.

Abstract Resource reservation protocol (RSVP) is a network‐control protocol used to guarantee Quality‐of‐Service (QoS) requirements for real‐time applications such as Voice‐over‐IP (VoIP) or Video‐over‐IP (VIP). However, RSVP was designed for end‐systems whose IP addresses do not change. Once mobility of an end‐system is allowed, the dynamically changing mobile IP address inevitably impacts on RSVP performance. Our study aims to first quantify the significance of this impact, and then propose a modified RSVP mechanism that provides improved performance during handoffs. Our simulations reveal that the deployment of standard RSVP over Mobile IPv6 (MIPv6) does not yield a satisfactory result, particularly in the case of VIP traffic. Fast Handovers for Mobile IPv6 (FMIPv6) was found to be providing the best performance in all tested scenarios, followed by Hierarchical Mobile IPv6 (HMIPv6) with a single exception: during low handoff rates with VoIP traffic, MIPv6 outperformed HMIPv6. We then designed a new RSVP mechanism, and tested it against standard RSVP. We found that the proposed approach provides a significant improvement of 54.1% in the Total Interruption in QoS (TIQoS) when deployed over a MIPv6 wireless network. For HMIPv6, performance depended primarily on the number of hierarchical levels in the network, with no improvement in TIQoS for single‐level hierarchy and up to 37% for a 5‐level hierarchy. FMIPv6 on the other hand, provided no room for improvement due to pre‐handoff signaling and the tunneling mechanism used to ensure a mobile node (MN)'s connectivity during a handoff, regardless of the RSVP mechanism used. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper we present a classification of fuzzy logic systems. We show how the fuzzy logic systems can be divided into different categories depending on their capabilities in handling uncertainties in input data and in rule base representations. We also summarize the strength and weakness of different types of fuzzy logic systems.

Flooding methods have been widely used by routing protocols to disseminate route information and control messages in wireless ad hoc and sensor networks both. However, most of the efficient flooding approaches proposed in the research literature are designed for ad hoc networks only, and they might not be suitable for wireless sensor networks due to their higher node densities, constrained energy and memory resources in sensor nodes. In this paper, we present a distributed low-cost flooding algorithm that is designed particularly for wireless sensor networks. We prove that our new algorithm has O(n + Delta) time and O(n) signaling message complexity, where n is the total number of nodes in a network and Delta is the maximum node degree. Moreover, we show that our low-cost algorithm has a constant approximation ratio, and its signaling message size is also bounded by a constant. Our simulation study demonstrates that our new algorithm not only generates fewer forwarding nodes, but it also uses much less number of signaling messages and has significantly smaller signaling message size than other algorithms published in the literature.

Mobile IP is the standardized IETF proposal to facilitate terminal mobility in wireless IP networks while RSVP is considered the integrated services standard in providing QoS guarantees. Since the former offers IP mobility and the later provides QoS guarantees, one would think that simply superimposing the two technologies would yield an optimum solution. This however is evidently not the case since RSVP was initially designed with static nodes in mind. A key issue relates to the IP-in-IP encapsulation of packets. This conceals the identity of RSVP packets from intermediate routers which fail to recognize them, thereby not reserving the necessary resources. Another issue arises in the event of a handoff where the interruption in QoS causes a notable degradation in application level performance. To address these issues, we propose a mobility extension which is fully compatible with RFC2205, and can therefore be implemented with minimal changes to end nodes. We assess our proposal by evaluating VoIP performance using parametric simulations. Our results indicate 27.9% improvement in QoS interruption when using mobile IP, and 12.5% with hierarchical mobile IP.

RSVP is considered the Integrated Services (IntServ) standard in providing QoS guarantees while Mobile IP is the standardized IETF proposal to facilitate terminal mobility in wireless IP networks. Since the former provides QoS guarantees and the later offers IP mobility, one would think that simply superimposing the two technologies would yield an optimum solution. This however is evidently not the case since RSVP was initially designed with static nodes in mind. This paper attempts to highlight the key issues involved with such an interaction by providing a testbed to quantitatively analyse how RSVP and MIPv6 hosts would likely to interact under different congestion scenarios. This paper also evaluates the potential effects and/or advantages of applying Mobile IPv6 enhancements such as Hierarchical Mobile IPv6 and Fast Handovers. As speculated, simply deploying default RSVP over MIPv6 does not yield an optimum result. More importantly, we show that in high congestion scenarios the IP-in-IP encapsulation introduced by HMIPv6 creates additional end-to-end packet delay and a slightly higher packet loss than MIPv6; thereby degrading the Mean Opinion Score (MOS). Based on our findings, MIPv6 with Fast Handovers (FMIPv6) operates best with RSVP due to minimal loss of packets during handoffs in addition to a smaller IP header when compared to HMIPv6. Finally we summarise the basic requirements needed to achieve an optimum QoS solution

In order to support mobile users, the basic Internet protocols have been extended with protocols (e.g., Mobile IPv6) for intercepting and forwarding packets to a mobile and possibly roaming node. Seamless roaming requires that users and applications do not experience loss of connectivity or any noticeable interruption in traffic. This is not only important for highly delay-sensitive traffic, but also for TCP-based traffic, as its performance is highly sensitive to packet loss and reordering. It is therefore imperative that handovers are initiated in such a way that the handover latency and packet loss is minimized. However, little is known about the performance of the proposed Mobile IPv6 approach in an actual network. In particular, it is not understood how different enhancement schemes for Mobile IPv6 influence essential performance metrics. This research paper is aimed at evaluating some of these enhancement techniques by extensive simulations to see how close we are to the ideal goal of attributing handover delay to the limitations of the physical hardware below the network layer.

Effective link layer triggering is the key in order to enable fast and reliable handovers across different access networks. In this paper, we investigate the use of signal strength as part of the trigger mechanism. We propose an analytical model to estimate the anticipation required in order to achieve a target handover packet loss performance, for both voice and video traffic patterns. Numerical results from the analytical model are presented and compared to simulation data obtained for realistic handover scenarios.

The mobile IPv6 handover process consists of three major stages: movement detection, duplicate address detection and binding signaling. Most of the research efforts for optimizing the handover process focuses on minimizing the delays caused by the first two stages. The delay introduced by binding signaling is largely ignored since the correspondent node is assumed to be stationary. However, this delay can be significant when two communicating nodes are both mobile and may roam across different networks simultaneously. mobile IPv6 requires that the binding signaling exchanged between the mobile node and the correspondent node to be authenticated using a procedure called return mutability test. The signaling involved in the return routability procedure is mandated to be redirected through the home agents to the most up-to-date addresses of the communicating nodes. This may result in a longer handover duration if the mobile and correspondent nodes are both far from their home agent, In this paper, we present our recommendation on the use of direct signaling without the need to have any redirection from a network element. We also present an analysis of the cost involved in utilizing direct and indirect signaling mechanisms.

This paper presents a new active queue management scheme, fuzzy explicit marking (FEM), implemented within the differentiated services (Diffserv) framework to provide the congestion control using a fuzzy logic control approach. Network congestion control remains a critical and high priority issue. The rapid growth of the Internet and increased demand to use the Internet for time-sensitive voice and video applications necessitate the design and utilization of effective congestion control algorithms, especially for new architectures, such as Diffserv. As a result, a number of researchers are now looking at alternatively schemes to TCP congestion control. RED (random early detection) and its variants are one of these alternatives to provide quality of service (QoS) in TCP/IP Diffserv networks. The proposed fuzzy logic approach for congestion control allows the use of linguistic knowledge to capture the dynamics of nonlinear probability marking functions and offer effective implementation, use of multiple inputs to capture the (dynamic) state of the network more accurately, enable finer tuning for packet marking behaviors (either dropping a packet or setting its ECN - explicit congestion notification - bit) for aggravated flows, and thus provide better QoS to different types of data streams, such as TCP/FTP traffic or TCP/Web-like traffic, whilst maintaining high utilization.

We investigate the performance of a classical constrained optimisation (CCO) algorithm and a constrained optimisation genetic algorithm (GA) for solving the bandwidth allocation for virtual paths (BAVP) problem. We compare throughput, fairness and time complexity of GA-BAVP and CCO-BAVP for several node topologies. The results on maximising the throughput obtained with GA-BAVP and CCO-BAVP are in close agreement, however when considering fairness GA-BAVP outperforms CCO-BAVP, especially for more complex topologies, like the 7-node network, without abundant link capacity. Convergence of the two algorithms appears similar, with GA-BAVP outperforming CCO-BAVP in initial stages, and vice-versa for longer time scales. However as the problem complexity increases the solution time for the genetic algorithm does not increase as fast as the classical constrained optimisation algorithm. A hybrid scheme is also introduced, combining the benefits of both algorithms. It exhibited better overall convergence rate but the same solution as CCO-BAVP.

Designing effective congestion control strategies for broadband networks is known to be difficult because of the variety of dynamic parameters involved such as link speeds, burstiness of the traffic, and the distances between traffic sources and switching nodes. We propose a traffic management scheme, which is insensitive to the propagation delay between the sources and switching nodes. We achieve this by combining connection admission control with a bandwidth allocation strategy. By seeking the cooperation of the available bit rate (ABR) sources (to limit their peak cell rate, PCR) at the time of admission of a variable bit rate (VBR) source to the network, the scheme eliminates the continuous congestion control. Our simulations show that the strategy is able to eliminate the ABR cell losses, achieves effective server and buffer utilization, and provides bounded delays to the VBR traffic (unlike the case without any controls).

Designing effective congestion control strategies for broadband networks is known to be difficult because of the variety of dynamic parameters involved such as link speeds, burstiness of the traffic, the distances between traffic sources and switching nodes. The uncertainties involved cause quantitative modeling of broadband networks almost impossible. Qualitative modeling techniques using the principles of fuzzy logic would lead to effective solutions. As part of our ongoing research program focusing on the development of intelligent control techniques for ATM networks, in this paper we present a regulator based on fuzzy logic control techniques for controlling congestion in broadband networks by keeping the ABR queue length around a reference value. Our simulations show that the controller is quite effective especially under network overload conditions.

We describe the fuzzy explicit fate marking (FERM) traffic flow control algorithm for a class of best effort service, known as available bit rate (ABR), proposed by the ATM Forum. FERM is an explicit rate marking scheme in which an explicit rate is calculated at the asynchronous transfer mode (ATM) switch and sent back to the ABR traffic sources encapsulated within resource management (RM) cells. The flow rate is calculated by the fuzzy congestion control (FCC) module by monitoring the average ABR queue length and its rate of change, then by using a set of linguistic rules. We use simulation to compare the steady-state and transient performance of FERM with EPRCA (a current favourite by the ATM Forum) in the presence of high priority variable bit rate (VBR) video and constant bit rate (CBR) in both a local-area network (LAN) and a wide-area network (WAN) environment. Our experiments show that FERM exhibits a robust behavior, even under extreme network loading conditions, and ensures fair share of the bandwidth for all virtual channels (VCs) regardless of the number of hops they traverse. Additionally, FERM controls congestion substantially better than EPRCA, offers faster transient response, leads to lower end-to-end delay and better network utilization.

High-speed LANs are beginning to migrate from shared media to switch based configurations (commonly based on ATM architectures). Because of the characteristics of ATM networks and nature of LAN traffic, there is a need for effective congestion control strategies to be developed in order to maintain the network utilization and throughput at optimum levels. We describe and report the simulation results of a novel congestion control strategy based on the principles of fuzzy logic control for the management of ABR traffic in ATM LANs. We propose that fuzzy logic control is an effective scheme for designing congestion control mechanisms in ATM LANs. Our simulations show that, proposed fuzzy backward congestion notification (FBCN) method is effective in minimizing cell losses at the switch queues and prevents time consuming packet retransmissions which can dramatically degrade the network throughput.