The emergence of generative artificial intelligence (GenAI) has brought both challenges and opportunities for education. In this paper, we propose a GenAI-empowered, group-based authentic assessment for a Network Engineering course. This group assignment promotes challenge-based learning (CBL) and leverages GenAI to enhance students' creativity, critical thinking, collaboration, and technical problem-solving skills, while also improving students' GenAI literacy through fostering their ability to effectively engage with GenAI tools. The authenticity of this assignment is reflected in two folds: 1) students engage in a real-world engineering challenge, roleplaying as network engineers, and 2) they develop essential skills for co-creating solutions using GenAI tools, a key competency for future engineers. The group assignment is structured into five stages, each aligned with Bloom's Taxonomy to progressively develop cognitive skills from understanding foundational knowledge to synthesis, evaluation, and creation. To mitigate challenges such as overreliance on GenAI tools and varying levels of digital literacy, we provide guidance on the responsible and ethical use of GenAI, design reflective assessment tasks with constructive feedback, and establish clear marking criteria that emphasise both the learning process and the final outputs of the assignment. Initial evaluation and feedback from trials have highlighted the effectiveness of using GenAI tools in addressing complex engineering challenges and the value of collaborating in a real-world engineering context. This innovative approach demonstrates the potential of GenAIempowered authentic assessments to enhance learning experiences in technical fields like Network Engineering.

This paper presents an AI-assisted approach that leverages Multimodal Large Language Models (MLLMs) to automate the generation of Multiple-Choice Questions (MCQs) for modules in engineering education. The system introduces a LOs extraction to MCQs generation pipeline, which extracts Learning Outcomes (LOs) from provided lecture notes and generates relevant MCQs with solutions and explanations based on the extracted LOs. By harnessing MLLMs' capabilities in vision and text comprehension, coupled with carefully crafted prompts from human educators, the tool efficiently produces context-relevant MCQs that can streamline teaching material development. The effectiveness of this AI-powered MCQ generation pipeline is investigated through experiments across a number of engineering modules with evaluations on the quality of the generated MCQs by human educators. The analysis of the evaluation results shows the AI tool's ability to generate MCQs that are well-aligned with LOs and exhibit strong contextual relevance, demonstrating the potential of AI-assisted approaches to enhance the efficiency of creating high-quality MCQs in engineering education. However, the variability in quality ratings across different aspects underscores the continued need for human expertise and oversight in the assessment design process. The findings provide useful insights into the capabilities and limitations of state-of-the-art multimodal language models in supporting assessment development in engineering education.

The 3rd Generation Partnership Project (3GPP) 5G Non-Terrestrial Networks (NTN) adopt Orthogonal Frequency Division Multiplexing (OFDM) to enable integrated space-ground networks via Low Earth Orbit (LEO) satellite global connectivity. However, the rapid movement of LEO satellites induces a significant non-uniform Doppler shift across subcarriers, resulting in the signal bandwidth changes that leads to sampling point offsets and severely impacting demodulation performance. Traditional Doppler compensation algorithms focus mainly on addressing the uniform Carrier Frequency Offset (CFO) caused by crystal oscillation. In LEO satellite broadband communication systems, the Sampling Frequency Offset (SFO), caused by the non-uniform Doppler frequency shift, can be tens of times greater than the CFO, leading to non-negligible phase rotation, inter-carrier interference (ICI), and inter-symbol interference (ISI). Consequently, a low-complexity algorithm is required to address the fast time-varying SFO — one of the core challenges in these systems. In this paper, we derive a closed-form expression for the signal distortion and propose a model-driven compensation method that leverages the predictability of satellite trajectories. The proposed method effectively removes phase rotation and mitigates ICI and ISI through fast Fourier transform (FFT) window adjustment and phase rotation compensation. The accuracy of the model is validated through extensive simulations and a real satellite communication trial. Results demonstrate that the proposed method supports higher-order modulations, leading to an average spectral efficiency improvement of approximately 50%. This pioneering research promises to ensure robust performance in dynamic LEO satellite environments.

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multiuser multiserver MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy tradeoff cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy tradeoff cost and its adaptability for various applications.

In response to the ever-increasing demands of users for delay-sensitive applications, issues on shortening the task completion time in the mobile edge computing (MEC) system has aroused widespread concern. From the perspective of task execution order, this work provide a task scheduling scheme for multiple edge nodes (EN) while federated learning (FL) is utilized for the collaboration of the ENs in the MEC system. First, to acquire an efficient execution order for the pending computational tasks that dynamically generated on one edge node, a task scheduling algorithm based on deep Q network (DQN) is proposed, which reduces the average task completion time. Then, based on the federated learning, of which characteristic matches the edge system well, we integrate an aggregation mechanism to take advantage of every participating edge node to obtain a set of global parameter that optimizes task completion delay for all nodes in the MEC system. Simulations verify the effectiveness and the superiority of the proposed algorithm in processing delay-sensitive tasks and analyze the key factors that contributes to the system performance.

This article proposes a novel methodology to detect malicious uniform resource locators (URLs) using simulated expert (SE) and knowledge-base system (KBS). The proposed study not only efficiently detects known malicious URLs but also adapts countermeasure against the newly generated malicious URLs. Moreover, this article also explored which lexical features are contributing more in final decision using a factor analysis method, and thus help in avoiding the involvement of human experts. Furthermore, we apply the following state-of-the-art machine learning (ML) algorithms, i.e., naïve Bayes (NB), decision tree (DT), gradient boosted trees (GBT), generalized linear model (GLM), logistic regression (LR), deep learning (DL), and random rest (RF), and evaluate the performance of these algorithms on a large-scale real data set of data-driven Web applications. The experimental results clearly demonstrate the efficiency of NB in the proposed model as NB outperforms when compared to the rest of the aforementioned algorithms in terms of average minimum execution time (i.e., 3 s) and is able to accurately classify the 107 586 URLs with 0.2% error rate and 99.8% accuracy rate.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with an MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things in a finite period. In this article, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling, and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

Language model (LM) plays an important role in natural language processing (NLP) systems, such as machine translation, speech recognition, learning token embeddings, natural language generation, and text classification. Recently, the multilayer long short-term memory (LSTM) models have been demonstrated to achieve promising performance on word-level language modeling. For each LSTM layer, larger hidden size usually means more diverse semantic features, which enables the LM to perform better. However, we have observed that when a certain LSTM layer reaches a sufficiently large scale, the promotion of overall effect will slow down, as its hidden size increases. In this article, we analyze that an important factor leading to this phenomenon is the high correlation between the newly extended hidden states and the original hidden states, which hinders diverse feature expression of the LSTM. As a result, when the scale is large enough, simply lengthening the LSTM hidden states will cost tremendous extra parameters but has little effect. We propose a simple yet effective improvement on each LSTM layer consisting of a large-scale Major LSTM and a small-scale Minor LSTM to break the high correlation between the two parts of hidden states, which we call Major-Minor LSTMs (MMLSTMs). In experiments, we demonstrate the LM with MMLSTMs surpasses the existing state-of-the-art model on Penn Treebank (PTB) and WikiText-2 (WT2) data sets and outperforms the baseline by 3.3 points in perplexity on WikiText-103 data set without increasing model parameter counts.

Information-centric networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current Internet protocol-address-based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, and caching the content at intermediate nodes to provide reliable, efficient data delivery, and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoT) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoT attracts both industry and research communities. IoT is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoT by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN-based caching, naming, security, and mobility approaches for IoT with appropriate classification. Furthermore, we present operating systems and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoT.

Text classification is a foundational task in many natural language processing applications. All traditional text classifiers take words as the basic units and conduct the pre-training process (like word2vec) to directly generate word vectors at the first step. However, none of them have considered the information contained in word structure which is proved to be helpful for text classification. In this paper, we propose a word-building method based on neural network model that can decompose a Chinese word to a sequence of radicals and learn structure information from these radical level features which is a key difference from the existing models. Then, the convolutional neural network is applied to extract structure information of words from radical sequence to generate a word vector, and the long short-term memory is applied to generate the sentence vector for the prediction purpose. The experimental results show that our model outperforms other existing models on Chinese dataset. Our model is also applicable to English as well where an English word can be decomposed down to character level, which demonstrates the excellent generalisation ability of our model. The experimental results have proved that our model also outperforms others on English dataset.

The dietary health of children is always a major issue in the field of health care, and one of the important parts is children's dietary assessment. However, the traditional approaches of dietary assessment are inefficient and hard to be popularized, and the data of dietary assessment are not utilized well. So the system is commissioned by Chinese Capital Institute of Pediatrics, which can solve the challenges of enhancing the efficiency of children's dietary assessment, improving the accuracy of estimation, and making full use of the data of dietary assessment. The system implements a complete process of Chinese children's dietary assessment. In this system, an approach of food weight estimation based on pictures taken by smartphones is proposed to make dietary assessment easily, an approach of error elimination based on machine learning is proposed to improve the accuracy of estimation, an approach of recommending foods to children that they need is proposed to utilize the data of dietary assessment meaningfully. The system can help hospitals and parents to assess children's diet easily, and provide them with a powerful reference to improve children's dietary health.

In cache enabled D2D communication networks, the cache space in a mobile terminal is relatively small compared with the huge amounts of multimedia contents. As such, how to cache the diverse contents in the multiple cache enabled mobile users, namely, the caching deployment, has substantial impact on network performance. In this paper, we propose a preference-aware caching based on cooperative game to incentivize users to cache contents for others, and then maximize the total cache utility by allocating cache space. We formulate a preference-aware cooperative game for cache utility maximization problem, which considers the user preference, physical distance, cache placement cost and cache space allocation. We divide the game into caching decision sub-game and cache space allocation sub-game. In caching decision sub-game, we obtain the Nash equilibrium solution by a heuristic caching decision method to minimize the total cost of getting objective contents. In cache space allocation sub-game, we solve the cache space allocation problem by using Lagrange method to finally maximize the total cache utility of the whole network. The convergence of the proposed algorithm is validated by simulation results. Compared with the existing caching schemes, the proposed scheme can achieve significant improvement on cache utility, cache hit ratio and content access delay gains.

Information-centric networking (ICN) approaches have been considered as an alternative approach to TCP/IP. Contrary to the traditional IP, the ICN treats content as a first-class citizen of the entire network, where names are given through different naming schemes to contents and are used during the retrieval. Among ICN approaches, content centric networking (CCN) is one of the key protocols being explored for Internet of Things (IoT), names the contents using hierarchical naming. Moreover, CCN follows pull-based strategy and exhibits the communication loop problem because of its broadcasting mode. However, IoT requires both pull and push modes of communication with scalable and secured content names in terms of integrity. In this paper, we propose a hybrid naming scheme that names contents using hierarchical and flat components to support both push and pull communication and to provide both scalability and security, respectively. We consider an IoT-based smart campus scenario and introduce two transmission modes: 1) unicast mode and 2) broadcast mode to address loop problem associated with CCN. Simulation results demonstrate that proposed scheme significantly improves the rate of interest transmissions, number of covered hops, name aggregation, and reliability along with addressing the loop problem.

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants' priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS), and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association, and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness, and QoS performance compared with several baseline schemes.

In cache enabled D2D communication networks, the cache space in a mobile terminal is relatively small compared with the huge amounts of multimedia contents. As such, a strategy for caching the diverse contents in a multiple cache-enabled mobile terminals, namely caching deployment, will have a substantial impact to network performance. In this paper, a user preference aware caching deployment algorithm is proposed for D2D caching networks. Firstly, based on the concept of the user preference, the definition of user interest similarity is given, in which it can be used to evaluate the similarity of user preferences. Then a content cache utility of a mobile terminal is defined by taking the communication coverage of this mobile terminal and the user interest similarity of its adjacent mobile terminals into consideration. The logarithmic utility maximization problem for caching deployment is formulated. Subsequently, we relax the logarithmic utility maximization problem, and obtain a low complexity near-optimal solution via dual decomposition method. The convergence of the proposed caching deployment algorithm is validated by simulation results. Compared with the existing caching placement methods, the proposed algorithm can achieve significant improvement on cache hit ratio, content access delay and traffic offloading gain.

Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW duration for diverse groups of devices would increase uplink energy consumption, delay and lower down the data rate. In this paper, we study a RAW optimization problem with a novel retransmission scheme that utilizes the next empty slot for retransmission in the uplink. The problem is formulated based on overall energy efficiency and delay of each RAW by applying probability theory and Markov Chain. To jointly optimize energy efficiency and delay, an energy-delay aware window control algorithm is proposed to adapt RAW size by estimating the number of time slots and internal slot duration in one RAW for different groups. The optimal solution is derived by applying Gradient Descent approach. Simulation results show that our proposed algorithm improves up to 113.3% energy efficiency and reduces 53.4% delay compared to the existing RAW.

The dense deployment of Femtocells within the Macrocell's coverage is expected to dominate the future of Long Term Evolution (LTE) networks. While Mobile Femtocells (Mobile-Femtos) could be the solution for vehicular networks when there is a need to improve the vehicular User Equipment (UE) performance by mitigating the impact of penetration loss and path-loss issues. The deployed Femtocells have operated in a co-channel deployment due to the scarcity of spectrums. This issue causes interference between Femtocells and Macrocells as well it causes extra overhead on the LTE networks because of the co-tire interference between adjacent Femtocells. In this paper two interference scenarios are considered, the interference between Mobile-Femto and Macrocell, and the interference between the Mobile Femtos themselves. Therefore, to avoid the generated interference between Femtocells, the controlled transmission powers as well as the coverage planning techniques have been discussed. While in the worst-case scenarios, a frequency reuse scheme has been proposed to avoid the generated interference effectively and dynamically between the Mobile-Femtos as well as their UEs and between the Macrocell UEs.

Energy efficiency is a crucial challenge in cluster-based capillary networks for Internet of Things (IoT) systems, where the cluster heads (CHs) selection has great impact on the network performance. It is an optimization problem to find the optimum number of CHs as well as which devices are selected as CHs. In this paper, we formulate the clustering problem into the CHs selection procedure with the aim of maximizing the average network lifetime in every round. In particular, we propose a novel CHs selection scheme based on QPSO and investigate how effective it is to prolong network lifetime and reserve the overall battery capacity. The simulation results prove that the proposed QPSO outperforms other evolutionary algorithms and can improve the network lifetime by almost 10%.

Massive devices and various applications imposes new challenges for Machine-to-Machine (M2M) communications to enable Internet of Things (IoT). In this paper, we investigate a QoS-aware joint access control and duty cycle control problem for M2M communications to optimise the overall network performance, including energy efficiency, end-to-end delay, reliability, throughput and fairness. We first model a practical hybrid M2M communication network and measure the overall network performance through a cost function. Then, an optimisation problem is formulated to minimise the long-term aggregated network cost. Further more, we overcome the non-convexity of the cost function and mathematically derive the optimal access control. Finally, we propose a distributed access control followed by a reinforcement learning (RL) based duty cycle control which adapts to various network dynamics without priori network information. Simulation results show that, the proposed joint access control and duty cycle control minimise the network long-term aggregated cost, while achieving fairness among cluster heads with QoS differentiation.

In Long Term Evolution (LTE)/LTE-Advanced (LTE-A) networks, hybrid access femtocells have been introduced as a tradeoff between closed and open access femtocells. However, quality of service (QoS) provisioning in hybrid access femtocells has become more challenging due to different user priority and traffic classes. Existing studies do not consider the LTE/LTE-A protocols and QoS priority of different traffic classes. In this work, a resource management scheme for prioritizing closed subscriber group (CSG) members and QoS provisioning in LTE/LTE-A hybrid access femtocells is proposed. Firstly, an admission control problem and a packet scheduling problem, which account for different user priority and QoS of different traffic classes, are formulated. A lexicographic greedy algorithm and a delay-prioritized scheduling algorithm are proposed to solve the two problems respectively. Simulation results show that the proposed scheme achieves a substantial performance gain in terms of packet loss and fairness for real-time traffic flows, albeit at the cost of lower fairness for non-real-time traffic flows.

Communications in Information-Centric Networking place more attention on WHAT data are being exchanged rather than WHO are exchanging them. A well-established approach of information centric networks is the Network of Information (NetInf) architecture, developed as part of the EU FP7 project SAIL. The security of NetInf has been fairly analysed in the literature. In particular, research efforts have been focusing on achieving data integrity and confidentially, source or publisher authenticity and authorization. This paper analyses some work in the literature to enforce authorized access to data in NetInf, highlights a potential security threat and proposes an enhancement to address the discovered threat. The new enhancement has been formally verified using formal method approach based on the Casper/FDR tool.

IEEE 802.15.4 beacon-enabled mode adopts duty cycle to achieve energy efficiency and provides an optional acknowledgement (ACK) mechanism to ensure the transmission reliability. However, frequently sending ACK introduces additional ACK transmission energy consumption and increases end-to-end delay. In this paper, we focus on a duty cycle optimisation problem with joint consideration on energy efficiency, end-to-end delay and reliability for IEEE 802.15.4 networks. We first formulate a cumulative ACK enabled duty cycle optimisation problem as an inventory control problem. Then, the optimal solution to the problem is derived by applying dynamic programming (DP). Furthermore, a low complexity delay-energy aware duty cycle control (DE-DutyCon) is proposed to reduce the computational complexity of implementing the control on computation limited sensor devices. The joint-cost upper bound of DE-DutyCon is also provided. DE-DutyCon achieves an exponential reduction of computational complexity compare with DP optimal control. Simulation results show that the proposed DE-DutyCon achieves close performance in terms of energy efficiency, end-to-end delay and packet drop ratio compare with DP optimal control under various network traffic.

the future of the Internet of Things (IoT) involves a huge number of node devices such as wireless sensors that can communicate in a machine-to-machine pattern, where devices will be globally addressed and identified. As the number of connected devices increased, the burden on the network infrastructure and the size of the routing tables and the efficiency of the current routing protocols in the Internet backbone increased as well. Recently, an IETF working group, along with the research group at Cisco, are working on a Locator/ID Separation Protocol as a routing architecture that provides new semantics for IP addressing, in order to simplify routing operations and improve scalability in the future of the Internet such as the IoT. In the light of the previous issue; this paperproposesan efficient security authentication anda key exchange scheme thatis suited for Internet of things based on Locator/ID Separation protocol. The proposed protocol method meets practicability, simplicity, and strong notions of security. The protocol is verified using Automated Validation Internet Security Protocols and Applications (AVISPA) which is a push button tool for the automated validation of security protocols and the achieved results showed that they do not have any security flaws.

Routing Protocol for Low Power and Lossy network (RPL) is the underlying routing protocol of 6LoWPAN standard to enable the integration of Wireless Sensor Network on the Internet. Security is an essential requirement for the protocol/standard to be public accepted. One major security issue of RPL is the non-tamper resistant nature of the wireless sensor device, which can make it easy to be compromised and leads to cryptography keys exposed. If this happens, the attackers can join the network as the internal legitimate users and can manipulate the network operation based on controlling the compromised nodes. These internal attackers can make severe consequences to the network performance. In this paper, we present a simulation-based study of the impacts of different types of such attacks on RPL. We consider several new internal threats towards RPL, namely Rank attack, Local Repair attack, Neighbour attack and DIS attack. Specifically we study how the network performance can be affected by those attacks through metrics such as end-to-end delay, delivery ratio and control overhead generated. To the best of our knowledge, this is the first paper that studies the vulnerability and the performance of RPL under these internal security threats.

Recent advances in mobile devices and network technologies have set new trends in the way we use computers and access networks. Cloud Computing, where processing and storage resources are residing on the network is one of these trends. The other is Mobile Computing, where mobile devices such as smartphones and tablets are believed to replace personal computers by combining network connectivity, mobility, and software functionality. In the future, these devices are expected to seamlessly switch between different network providers using vertical handover mechanisms in order to maintain network connectivity at all times. This will enable mobile devices to access Cloud Services without interruption as users move around. Using current service delivery models, mobile devices moving from one geographical location to another will keep accessing those services from the local Cloud of their previous network, which might lead to moving a large volume of data over the Internet backbone over long distances. This scenario highlights the fact that user mobility will result in more congestion on the Internet. This will degrade the Quality of Service and by extension, the Quality of Experience offered by the services in the Cloud and especially multimedia services that have very tight temporal constraints in terms of bandwidth and jitter. We believe that a different approach is required to manage resources more efficiently, while improving the Quality of Service and Quality of Experience of mobile media services. This paper introduces a novel concept of Cloud-Based Mobile Media Service Delivery in which services run on localized public Clouds and are capable of populating other public Clouds in different geographical locations depending on service demands and network status. Using an analytical framework, this paper argues that as the demand for specific services increases in a location, it might be more efficient to move those services closer to that location. This will prevent the Internet backbone from experiencing high traffic loads due to multimedia streams and will offer service providers an automated resource allocation and management mechanism for their services.

For communication in Next Generation Networks, highly-developed mobile devices will enable users to store and manage a lot of credentials on their terminals. Furthermore, these terminals will represent and act on behalf of users when accessing different networks and connecting to a wide variety of services. In this situation, it is essential for users to trust their terminals and for all transactions using them to be secure. This paper analyses a number of the Authentication and Key Agreement protocols between the users and mobile terminals, then proposes a novel device authentication protocol. The proposed protocol is analysed and verified using a formal methods approach based on Casper/FDR compiler.

Being able to understand human behaviour and monitoring daily life activities is seen as a significant approach for alleviating functional decline among elderly people. The aim of the research work presented in this paper is to investigate a mechanism that can recognise high level activities and behaviour of low entropy people in order to help them improve their health related daily life activities by using wireless proximity data (e.g. Bluetooth, Wi-Fi). A number of scenarios and experiments are designed to prove the validity of the proposed methodology. Using wireless proximity data for activity recognition enhances the intrusion into personal privacy and helps exploiting important structures in human behaviour.

An approach to generate generalized pairwise Z-complementary (GPZ) codes, which works in pairs in order to offer a zero correlation zone (ZCZ) in the vicinity of zero phase shift and fit extremely well in power efficient quadrature carrier modems, is introduced in this letter. Each GPZ code has MK sequences, each of length 4NK, where M is the number of Z-complementary mates, K is a factor to perform Walsh-Hadamard expansions, and N is the sequence length of the Z-complementary code. The proposed GPZ codes include the generalized pairwise complementary (GPC) codes as special cases.

This paper presents a new approach to pixel domain Wyner-Ziv (PDWZ) video coding by using weighted motion compensated refinement (WMCR) embedded into turbo decoder to efficiently optimize side information. The proposed system is designed by combining a relatively simple interpolation model for initial side information plus embedded refinement process to achieve better performance of system as a whole. In order to explore practical application of PDWZ in wireless environment, we adopt the 3GPP standardized turbo codec as Slepian-Wolf codec in PDWZ system. Experimental results showed that the proposed system have noticeable improvement in RD performance and reduced complexity of whole system.

Routing protocols face many challenges in Mobile Ad Hoc Networks (MANETs) due to the dynamic nature of wireless medium and unpredictable topological changes. This paper proposes On-Demand Link-Weight (ODLW) routing protocol. ODLW selects an optimum route on the basis of link reliability taking in consideration the link bandwidth, delay and node lifetime. The approach is based on Cross- Layer design where Physical Layer parameters are used as inputs for decision making at the routing layer. The simulation results show a significant improvement over AODV in terms of Network load, Route Discovery time and End-to-end delay.