Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring.

Fall detection and recognition play a crucial role in enabling timely medical interventions for people who are at risk of falls, especially among vulnerable populations like older adults and those with mobility limitations. In this article, a cost-effective integrated sensing and communication system, namely, FallDR, is presented for fall detection and recognition using ultrawideband communication. First, we collected the time of flight information of falls (four types) and nonfall events by 10 participants using FallDR. We then proposed a convolutional neural network incorporated with squeeze-and-excitation blocks to detect and recognize falls based on fall trajectories. It proves that the proposed model is accurate, energy-efficient, and lightweight to achieve 100% accuracy in fall detection and recognition. Our proposed solution is proven to be highly robust against environmental changes, such as interference, distance, and direction changes. Further tests in an office showed that FallDR could achieve nearly 100% accuracy, even when the environment was changed. FallDR efficiently employs the characteristics of fall trajectory and the advanced modeling ability of the neural network. We have published our archived data sets and code for comparisons and improvements.

In order to provide communication services in a more efficient manner, and instead of using low-orbit communication satellites, we investigate an integrated navigation and communication (INAC) network by medium-orbit navigation satellites. In this article, non-orthogonal multiple access (NOMA) is investigated for facilitating the INAC information. In order to improve the received signal power level, we then introduce a reconfigurable intelligent surface (RIS)-aided INAC network. The navigation and communication signals can be reflected to the users located in the city center. Based on the different power allocation factors, navigation-oriented-INAC (NO-INAC) and communication-oriented-INAC (CO-INAC) are proposed to meet different application scenarios. The bit error ratio (BER) is calculated to illustrate the performance of both NO-INAC and CO-INAC. We analyze the results of single-point positions and multi-point positions. The numerical results demonstrate that: 1) The RIS-aided INAC network is able to successfully reflect the navigation and communication signals from the occluding satellite. 2) The proposed RIS-aided INAC network provides a new solution for satellite communications in a more efficient manner.

Radar-based human activity recognition is attracting a wide range of interest from both industry and academia because of its through-wall ability, privacy-preserving capability, and device-free detection. Currently, most radar-based systems consider signal analysis and feature extraction in the frequency domain or the temporal domain independently without fusing them together. In this article, in order to model both frequency properties and temporal profiles of human activity, we proposed a spectro-temporal network (STnet) that integrates a temporal convolutional network (TCN) and a convolutional neural network (CNN). It can extract temporal patterns and micro-Doppler features from radar signals for human activity recognition. In the experiments, two radar sensors and one base station were used to build a low-power wireless radar sensor network. Fifteen activities were investigated in a real kitchen scenario by using this radar sensor network. Frequency spectrograms were obtained after signal processing using a short-time Fourier transform (STFT). They were further segmented using a short sliding window (2.5 s), which enables a very small latency. The proposed STnet achieved 99.64% overall accuracy (OA) in testing, which is superior to the other three networks that we implemented in this work. Our work also can be used as a generic solution to other sensor-based (wearable sensors, WiFi channel state information (CSI), etc.) activity recognition.

Automatic fall detection and recognition are challenging problems. In this paper, a novel solution is proposed based on the trajectories of human falls by using the ultra-wideband (UWB) communication system and machine learning methods for fall detection and recognition. Most previous studies of fall detection based on active UWB sensing used electromagnetic signals directly, which may bring problems like radar clutter, signal coupling, multi-path, fading, and interference. Our proposed method only uses human falls trajectories by passive UWB sensing, which achieved fall recognition performance of 93.26% by using the support vector machine with RBF kernel function (SVM-RBF). Compared with previous research, the superiority of this study is that our solution is robust against interference and environmental changes, which means it is reliable for real-world applications. The archived UWB datasets and code have been already published, which may provide the basis for the comparison of techniques and improvements.

Global Navigation Satellite System (GNSS) is widely applied in the next generation wireless networks, where high-precision positioning is required. However, due to the impact of blockages, the high-precision positioning signals are blocked in the ultra-dense networks. In this article, we introduce a RIS-aided GNSS network, where more navigation links can be reflected to the users located at the city center. In order to evaluate the performance of the network, we analyze the results of multi-point location respectively. We also analyze the skyplot and dilution of precision (DoP) of the RIS-aided GNSS network. The numerical results show that: 1) The RIS assisted GNSS network is able to successfully reflect the navigation signals from the occluding satellite and realize the location calculation in the city center. 2) The location of RIS affects the positioning accuracy, and the appropriate RIS location is useful to improve the positioning accuracy.

In this article, a cost-effective ultrawideband (UWB) communication system for gesture recognition in a smart home environment is proposed, which uses gesture trajectories and a deep learning model. Most previous studies of gesture recognition using the UWB technology used electromagnetic signals directly, which may bring problems, such as radar clutter, signal coupling, multipath, fading, and interference. However, instead of using UWB’s high-frequency pulse signals, the proposed method only uses gesture trajectories by data positioning. To this end, first, a data set of four gesture activities was created. Then, this data set was trained using a convolutional neural network (CNN) integrated with a squeeze-and-excitation (SE) block, namely, the SE-Conv1D model. Finally, the system was prototyped to interact with appliances in practical smart homes. The experimental data was used to demonstrate the superiority of the SE-Conv1D model in comparison with four baselines: 1) support vector machines; 2)$K$-nearest neighbor; 3) random forest; and 4) binarized neural networks. Experimental results show that all collected gesture activities are correctly recognized with an overall accuracy of over 95%, among which the proposed SE-Conv1D model achieves the best accuracy of 99.48%. The proposed system is a complete end-to-end sensing system specifically designed for tracking and recognizing human gestures, which is robust against interference and changes in distance or direction. In addition, the proposed system can tackle the device selection problems for smart homes, which means it is reliable for real-world applications.

A novel approach is proposed for multimodal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet-of-Things (IoT) node equipped with machine learning capabilities; CA aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multisensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching dynamic Bayesian network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A growing neural gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely, distributed Markov jump particle filter (D-MJPF), employed for joint state estimation and abnormality detection. The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, first the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analyzed for the collective abnormality estimation of the agents. Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well.

The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness (CA) in a network of intelligent agents. A specific collective self-awareness functionality is considered, namely, agent-centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent. Data-driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent, is used to allow the agents in the network to reach synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithm's reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Performances of the DBN-based abnormality detection models under different channel and source conditions are discussed. The effects of distances among agents and of the delays and packet losses are analyzed in different scenario categories (urban, suburban, and rural). Real data sets are also used acquired by autonomous vehicles performing different tasks in a controlled environment.

Motion trajectories contain rich information about human activities. We propose to use a 2-D LIDAR to perform multiple people activity recognition simultaneously by classifying their trajectories. We clustered raw LIDAR data and classified the clusters into human and nonhuman classes in order to recognize humans in a scenario. For the clusters of humans, we implemented the Kalman filter to track their trajectories which are further segmented and labeled with corresponding activities. We introduced spatial transformation and Gaussian noise for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition (HAR). Finally, we built two neural networks, including a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to classify trajectory samples into 15 activity classes collected from a kitchen. The proposed TCN achieved the best result of 99.49% in overall accuracy. In comparison, the TCN is slightly superior to the LSTM network. Both the TCN and the LSTM network outperform the hidden Markov model (HMM), dynamic time warping (DTW), and support vector machine (SVM) with a wide margin. Our approach achieves a higher activity recognition accuracy than the related work.

In this study, we propose a novel, context-based, location-aware algorithm for identifying low-level micro-activities that can be used to derive complex activities of daily living (ADL) performed by home-care patients. This identification is achieved by gathering the location information of the target user by using a wearable beacon embedded with a magnetometer and inertial sensors. The shortcomings of beacon-signal stability and mismatch issues in magnetic-field sequences are overcome by adopting a hybrid, three-phase approach for deducing the locus of micro-activities and their associated zones in a smart home environment. The suggested approach is assessed in two different test environments, where the main intention is to map the location of a person performing an activity with pre-defined house landmarks and zones in the offline labeled database. In addition to the recognition of low-level activities, the proposed method also identifies the person's walking trajectory within the same zone or between different zones of the house. The experimental results demonstrate that it is possible to achieve centimeter-level accuracy for the recognition of micro-activities and to achieve the classification accuracy of 85% for trajectory prediction. These results are encouraging and imply that the collection of accurate low-level information for ADL recognition is possible using integration of inertial sensors, magnetic field and Bluetooth low energy (BLE) technologies from the wearable beacon without relying on other infrastructural sensors.

Human activity detection plays a crucial role in the recognition of activities of daily living (ADLs). In the past ten years, research on activity detection in the home was achieved through the data aggregation from several different sensors (presence sensors, door contacts, appliances tagging, cameras, wearable beacons, mobile phones, etc.). However, the cost of deployment and maintenance of a multitude of sensor devices and the intrusiveness they can infer are quite high. Research on minimal and non-intrusive sensing for recognition of ADLs are vital for the future of remote care. In this paper, we propose a minimal and non-intrusive low-power low-cost radar-based sensing network system that uses an innovative approach for recognizing human activity in the home. We applied our novel approach to the challenging problem of kitchen activity recognition and investigated fifteen different activities. We designed and trained a deep convolutional neural network (DCNN) that classifies different activities based on their distinct micro-Doppler signatures. We achieved an overall classification rate of 92.8% in activity recognition. Most importantly, in nearly real-time, our approach successfully recognized human activities in more than 89% of the time.

Conventional Radio Frequency (RF) based fingerprinting still remains one of the most popular methods amongst other indoor positioning techniques due to its inherent accuracy and reliability. However, not much prominence has been shown in analyzing certain factors that may affect the outcome of the fingerprinting method while designing the localization system. In this paper, we conduct a study to infer if a reduced number of receivers equipped with higher gain antennas can provide improved Bluetooth Low Energy (BLE) fingerprinting performance in a complex indoor environment. The evaluation is performed in a standard domestic apartment with an activity centric approach using a single wearable beacon and multiple receivers. A rank based route selection algorithm is used to list the candidate positions or routes that indicate the most likely path on which the subject was travelling. Furthermore, we discuss the benefits of implementing the inverse fingerprinting method with a trajectory based prediction model and also examine the effect of surrounding electrical interference. Experimental results indicate that an increased antenna gain in addition to deploying an adequate number of receivers have a positive effect on the overall ranking accuracy.

For unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) networks, we propose a unified three-dimensional (3D) spatial framework in this paper to model a general case that uncovered users send messages to base stations via UAVs. More specifically, the locations of transceivers in downlink and uplink are modeled through the Poisson point processes and Poisson cluster processes (PCPs), respectively. For PCPs, Matern cluster and Thomas cluster processes, are analyzed. Furthermore, both 3D blockage processes and 3D antenna patterns are introduced for appraising the effect of altitudes. Based on this unified framework, several closed-form expressions for the coverage probability in the uplink and downlink, are derived. By investigating the entire communication process, which includes the two aforementioned phases and the cooperative transmission between them, tractable expressions of system coverage probabilities are derived. Next, three practical applications in UAV networks are provided as case studies of the proposed framework. The results reveal that the impact of thermal noise and non-line-of-sight mmWave transmissions is negligible. In the considered networks, mmWave outperforms sub-6 GHz in terms of the data rate, due to the sharp direction beamforming and large transmit bandwidth. Additionally, there exists an optimal altitude of UAVs, which maximizes the system coverage probability.

Device-free or off-body sensing methods, such as Lidar, can be used for location-driven Activities during Daily Life (ADL) recognition without the need for a mobile host such as a human or robot to use on-body location sensors. Because if such an attachment fails, or is not operational (powered up), when such mobile hosts are device free, it still works. Hence, this paper proposes an innovative method for recognizing ADLs using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a low-cost, 360°rotating 2D Lidar device. We researched, developed, deployed and validated the system. The results indicate that it can provide a centimeter-level localization accuracy of 88% when recognizing 17 targeted location-related daily activities.

In this paper a novel cloud-based Emergency Warning System (EWS) is described, using India as a primary case study. India is a country with nearly two dozen officially recognized languages and divergent communication technologies spread across a large geographical area. As of yet, this diversity has made the deployment of a single nationwide EWS near impossible. To address this deficiency, we have developed an EWS with adaptation as a central design tenet. This adaptation occurs on three levels: Dissemination adaptation addresses civilians' heterogeneous communication technologies; information adaptation morphs warnings to best reflect the capabilities of these communication technologies and users; while presentation adaptation is used to render information to the user in the most appropriate manner. We have developed a full cloud-based prototype, including the full EWS infrastructure and a civilian Android app.

Carbon Monoxide (CO) induced by traffic pollution is highly dynamic and non-linear. In a pilot research, we collected some fine-grained 1Hz CO pollution data from a residential road and a busy motorway in Hyderabad, India, in preparation of the deployment of a larger scale, longer term wireless sensor monitoring system. Power conservation is an important issue as the sensor nodes are battery operated. We studied the characteristics of the collected data and designed an adaptive sampling algorithm, Bi-Scale temporal sampler, which adapts the sampling frequency to the statistics collected in real time. This design has incorporated practical engineering considerations including minimising electronic noise, sensor warm-up time and data characteristics. Results show that Bi-Scale sampler achieves better energy saving and statistical deviation ratio for our requirements than burst sampling and eSENSE sampling strategies, which are techniques popularly used in environmental monitoring applications.

We propose a real-time adaptive- spatial sampling technique for the efficient collection of fine grained data in wireless sensor networks. The collection of fine grained data can incur high energy costs. This energy costs can be reduced by exploiting the spatial correlations of adjacent nodes, where only the most dominant nodes collect the data. We show that, using concepts developed in Random Matrix Theory, it is possible to determine the dominant nodes which enable to process noisy data in a time efficient, scalable, decentralized manner. The proposed technique has been validated using spatially interpolated pollution datasets giving good results in terms of data reduction and accuracy.

Many software systems operate across different geographically distributed hardware platforms, operating systems and programming languages. Publish/subscribe based Message Oriented Middleware (MOM) provides loose coupling and an efficient, asynchronous and scalable way of communication. However, as the complexity of such systems increase, manual verification of reconfiguration policies becomes unrealistic. The task calls for automated means of proof-checking configuration information in order to improve the reliability of large-scale MOM systems. This paper proposes a new model checking approach with temporal logic specifications to design and verify a system configuration. Model checking is a powerful technique, however the creation of appropriate finite state models for the systems being checked are complex and difficult to use in practice by non-formalists. The research presented in this paper finds suitable abstractions that reduce the system to a finite state model. The tools we developed for the generation of such models can be easily used by non-formalists. The systems models created using our techniques manages state explosion thanks to the choices of our abstractions. An example of the use of our tools and techniques is presented for a 50 node MOM, where the reachability of all topics and the presence of loops are proof-checked.

Air pollution data exhibit characteristics like long range correlations and multi-fractal scaling that can be exploited to implement an energy efficient, adaptive spatial sampling technique for pollution sensor nodes. In this work, we present a) results from de-trended fluctuation analysis to prove the presence of non-linear dynamics in real pollution datasets gathered from trials carried out in Cyprus, b) a novel Multi-scale Nearest Neighbors based Adaptive Spatial Sampling (MNNASS) technique that determines the predictability and in turn the directional influences between data from different sensor nodes, and c) performance analysis of the algorithm in terms of energy savings and measurement accuracy.

A probabilistic approach for outdoor location estimation using GSM received signal strength (RSS) from base stations (BSs) is presented. The proposed approach first divides the region of interest into different clusters based on deviations from the path loss model for each RSS component. In each cluster, the proposed algorithm uses principal component analysis (PCA) to intelligently transform RSS into new uncorrelated dimensions. This retains accuracy by not losing the substantial RSS correlations in each cluster, but also accommodates the different RSS distributions in each cluster. Our experiments are conducted in a real GSM outdoor environment. The proposed approach is compared with a traditional probabilistic algorithm for three different area partitioning methods. The experimental results show that the positioning accuracy is significantly improved and our clustering scheme gives good support for location estimation. Furthermore, it also can be concluded that the clustering scheme created by using deviation RSS based on Mahalanobis distance performs better than that using deviation based on Euclidean distance in a complex environment. What's more, the proposed method can reduce the number of training data used while maintaining the accuracy required.

A pollution data collection exercise was conducted in Hyderabad, India in February, 2012. Fifteen bespoke Carbon Monoxide (CO) monitors were deployed across a small section of a busy highway to collect data for an urban environmental engineering study targeting traffic-generated pollution. The monitors were used to record CO concentrations and temperature; however, in spite of the fact that the monitors were calibrated in advance of deployment, interpretation of the data collected has proved to be challenging. This paper reports the findings of the experiment and proposes a bias-adjustment separation technique that provides a consensual baseline for all the monitors. The result is that spatial variation in the distribution of CO can be studied at snapshots of time. Moreover, the cross-correlations between sensors can be reliability extracted after the bias adjustment.

This paper presents results for an approach for indoor location estimation that integrates received signal strength (RSS) data from both WiFi and GSM networks. Previous work has focused on relatively small indoor environments. In many potential applications, getting approximate location information, such as in which room the mobile user is, is adequate. A hierarchical clustering method is used to partition the RSS space. To choose the best transmitters in a partition, we assess the amount of RSS variance that is attributable to different base stations (BSs) or access points (APs) by transforming the RSS tuples into principal components (PCs). This allows us to retain most of the useful information of detectable transmitters in fewer dimensions. In our experiments, we collected WiFi and cellular RSS on the 2nd and 3rd-floor electronic engineering (EE) building in Queen Mary campus. The experiment results show that the proposed method can provide a good accuracy of room prediction, especially when we integrate WiFi RSS with GSM RSS together to do the positioning.

To increase frequency efficiency in cellular communication networks, this paper describes a cell coloring based distributed frequency allocation approach (C-DFA) for all kinds of cellular networks. C-DFA has high computational efficiency and is simpler to realize than other distributed approaches. Building on C-DFA, a distributed dynamic fractional frequency allocation (DDFFA) algorithm is designed for IEEE 802.16j supported Relay Based Cellular Networks (RBCN). It is shown that: a) C-DFA can better realize frequency efficiency and network resilience compared to centralized traditional distributed frequency allocation approach. b) DDFFA can significantly increase frequency efficiency to provide high capacity and throughput to the RBCN though at the cost of extra computation and BS-BS communication. The evaluations and analysis are based on moderate and high user congestion RBCN scenarios.

This paper focuses on the problem of how to protect personal data and privacy in the context of internet-centric services. Two main challenges are considered: how to enable individuals to express data protection requirements on their data in a disclosure request; and how to ensure data is actually protected and processed according to the intended purpose of use after being disclosed. As part of our solution, we introduce the notion of a distinctive online service and architectural component, called the Privacy and Security Broker (PSB), responsible for the protection of personal data. The PSB enables a user to express their data protection requirements and translates them into "Data Protection Property Policies" (DPPPs). A high level architecture and the corresponding protocols involving the interaction of the main actors of our solution are presented.

In this paper, we propose a novel system architecture to realize OFDMA in ad hoc networks. A partial time synchronization strategy is presented based on the proposed system model. This proposed scheme can support concurrent transmission without global clock synchronization. We also propose a null subcarrier based frequency synchronization scheme to estimate and compensate frequency offsets in a multiple user environment. The simulation results show a good performance of our proposed synchronization scheme in terms of frequency offset estimation error and variance.

In order to provide a more resilient communication for a Neighborhood Watch type (NHW) system, a mobile middleware architecture using delay-tolerant network (DTN) technology and publish/subscribe concepts is described. The purpose of the middleware is to add resilience and reach to wireless communications, and also to reduce the battery drainage of the mobile devices, hence extending their operational lifetime. A prototype developed on the Android mobile platform is presented and the effects of multiple radio access, routing algorithm and application activity on energy efficiency are investigated.

This paper describes an approach to providing more accurate estimates of current radio coverage and real-time monitoring of coverage changes over time, in the context of self-organizing networks (SON). Radio coverage probability models based on received signal strength (RSS) from base stations (BSs) in an outdoor environment are created. Clustering is used to partition the RSS space and a nonparametric probability approach is used to reliably estimate the radio coverage in each cluster, that is also used to test for discrepancies in the RSS coverage that may occur over time. It is assumed that data can be collected periodically from the physical environment. The analysis of discrepancies is based on models constructed from historical data and monitoring of current RSS from the mobile stations (MSs). The performance is evaluated using data generated from a network planning tool for a real environment.

We present the design of a novel adaptive sampling technique called Exponential Double Smoothing-based Adaptive Sampling (EDSAS), in which the temporal data correlations provide an indication of the prevailing environmental conditions and are used to adapt the sensing rate of a sensor node. EDSAS uses irregular data series prediction to reduce sampling rate in combination with change detection to maintain data fidelity. The prediction method employs Wright's extension to Holt's method of Exponential Double Sampling (EDS) coupled with a change detection mechanism based on exponentially weighted moving averages (EWMA). The main advantages of EDSAS are that it does not require heavy computation, incurs low memory and communication overhead and the prediction model can be implemented with ease on resource constrained sensor nodes. EDSAS has been evaluated by using real urban road traffic Carbon Monoxide (CO) pollution datasets and has been compared and shown to give better results for performance metrics like sampling fraction and miss ratio. We have also undertaken analysis of the pollution data based on the information received and shown that EDSAS scores over other published technique called e-Sense in capturing the underlying characteristics of the real data.

An architecture for wireless communication between control centers and registered users in emergency situations when the cellular network is not operational is described. It is based on delay tolerant networks (DTN) and enables mobile users with multiple radio interface technologies to communicate across heterogeneous network technologies. With the help of asynchronous bundles passing from hop to hop, short message service (SMS), multimedia messaging and walkie-talkie communication services are provided. The security issues for deployment of DTN communication applications using this architecture are also described. To assess the performance of the system, results from simulations for the degree of message penetration of broadcasts into the community and the probability that a control centre receives uplink messages are presented for different user densities, routing protocols on the system. The approach has been implemented on the Android mobile platform.

A variety of wireless network technologies have been developed and deployed, including GSM, UMTS, WiFi and WiMAX. The advantages of having an integrated heterogeneous wireless network environment include seamless communications, joint resource management and adaptive quality of service. In such environment, operators would not need to reject the service requests, but redirect them to appropriate networks. However, the sought aims of a heterogeneous network system still have many pending issues. One of them is the selection of the most appropriate radio access network (RAN) according to the requested service and the context information about the user and the networks. We aim to develop efficient RAN selection algorithms to facilitate radio access optimisation for future heterogeneous network system. The simulation results show that our RAN selection algorithm can improve the network performance.

In recent years, a variety of wireless network technologies have been developed and deployed, including UMTS, WiFi, and WiMAX. The overlapping of different networks creates heterogeneous wireless environments, which can enable seamless communications, joint resource management and adaptive quality of service. In such environments, operators do not need to reject user requests, but redirect them to the most appropriate networks. However, heterogeneous wireless systems still have many pending issues to solve. One of them is the selection of the most appropriate radio access network when receiving a service request. This paper addresses this issue by proposing an adaptive and efficient algorithm. The simulation results show that the proposed radio access network selection algorithm can improve the network performance and capacity.

Recently, mobile ad hoc networks have obtained a growing interest because of their advantages in many practical applications. One of the crucial points is the quality-of-service (QoS) routing for real-time applications, which requires sufficient and constant bandwidth. Therefore, a routing protocol should also consider the definition of an efficient medium access control (MAC) scheme in a cross layer design. A QoS routing protocol is proposed in this paper to calculate, allocate and reserve resources for CDMA based ad hoc networks with route request. The protocol is called CDMA Bus Lane routing protocol

As the demand for mobile services has increased, the need for an efficient allocation of channels is essential to ensure good performance, given the limited spectrum available. Techniques for increasing flexibility in radio resource acquisition are needed to handle the heterogeneity of services and bit rates to be supported in the forthcoming generations of mobile communications. To improve the performance and efficiency of the channel allocation, we propose the use of a particular agent architecture that allows base stations to be more flexible and intelligent, including planning to attempt to balance the load in advance of reactive requests. The simulation results prove that the use of intelligent agents controlling the allocation of channels is feasible and the agent negotiation is an important feature of the system in order to improve perceived quality of service and to improve the load balancing of the traffic.

Resource flexibility is one of the most important requirements in the next generation of mobile communications. Techniques are required to increase the flexibility of the network to deal with new services and the consequent new traffic profiles and characteristics. This paper investigates some of the drawbacks of fully reactive channel allocation schemes and proposes a more flexible scheme using intelligent agents that will lead to an efficient solution under moderate and heavy loads. The agent architecture adopted provides greater autonomy to the base stations and a method for allowing cooperation and negotiation between them; this autonomy and cooperation allows an increase in flexibility to deal with new traffic situations and an increase of the robustness of the network as a whole.