Secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks

Data aggregation in wireless sensor networks is employed to reduce the communication overhead and prolong the network lifetime. However, an adversary may compromise some sensor nodes, and use them to forge false values as the aggregation result. Previous secure data aggregation schemes have tackled this problem from different angles. The goal of those algorithms is to ensure that the Base Station (BS) does not accept any forged aggregation results. But none of them have tried to detect the nodes that inject into the network bogus aggregation results. Moreover, most of them usually have a communication overhead that is (at best) logarithmic per node. In this paper, we propose a secure and energy-efficient data aggregation scheme that can detect the malicious nodes with a constant per node communication overhead. In our solution, all aggregation results are signed with the private keys of the aggregators so that they cannot be altered by others. Nodes on each link additionally use their pairwise shared key for secure communications. Each node receives the aggregation results from its parent (sent by the parent of its parent) and its siblings (via its parent node), and verifies the aggregation result of the parent node. Theoretical analysis on energy consumption and communication overhead accords with our comparison based simulation study over random data aggregation trees.

Distributed data possession checking for securing multiple replicas in geographically-dispersed clouds

Distributing multiple replicas in geographically-dispersed clouds is a popular approach to reduce latency to users. It is important to ensure that each replica should have availability and data integrity features; that is, the same as the original data without any corruption and tampering. Remote data possession checking is a valid method to verify the replicass availability and integrity. Since remotely checking the entire data is time-consuming due to both the large data volume and the limited bandwidth, efficient data-possession- verifying methods generally sample and check a small hash (or random blocks) of the data to greatly reduce the I/O cost. Most recent research on data possession checking considers only single replica. However, multiple replicas data possession checking is much more challenging, since it is difficult to optimize the remote communication cost among multiple geographically-dispersed clouds. In this paper, we provide a novel efficient Distributed Multiple Replicas Data Possession Checking (DMRDPC) scheme to tackle new challenges. Our goal is to improve efficiency by finding an optimal spanning tree to define the partial order of scheduling multiple replicas data possession checking. But since the bandwidths have geographical diversity on the different replica links and the bandwidths between two replicas are asymmetric, we must resolve the problem of Finding an Optimal Spanning Tree in a Complete Bidirectional Directed Graph, which we call the FOSTCBDG problem. Particularly, we provide theories for resolving the FOSTCBDG problem through counting all the available paths that viruses attack in clouds network environment. Also, we help the cloud users to achieve efficient multiple replicas data possession checking by an approximate algorithm for tackling the FOSTCBDG problem, and the effectiveness is demonstrated by an experimental study. © 2011 Elsevier Inc.

A secure and efficient data sharing framework with delegated capabilities in hybrid cloud

Hybrid cloud is a widely used cloud architecture in large companies that can outsource data to the publiccloud, while still supporting various clients like mobile devices. However, such public cloud data outsourcing raises serious security concerns, such as how to preserve data confidentiality and how to regulate access policies to the data stored in public cloud. To address this issue, we design a hybrid cloud architecture that supports data sharing securely and efficiently, even with resource-limited devices, where private cloud serves as a gateway between the public cloud and the data user. Under such architecture, we propose an improved construction of attribute-based encryption that has the capability of delegating encryption/decryption computation, which achieves flexible access control in the cloud and privacy-preserving in datautilization even with mobile devices. Extensive experiments show the scheme can further decrease the computational cost and space overhead at the user side, which is quite efficient for the user with limited mobile devices. In the process of delegating most of the encryption/decryption computation to private cloud, the user can not disclose any information to the private cloud. We also consider the communication securitythat once frequent attribute revocation happens, our scheme is able to resist some attacks between private cloud and data user by employing anonymous key agreement.

Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network

This paper presents the first study on scheduling for cooperative data dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication environment. We formulate the novel problem of cooperative data scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current neighboring vehicles and the identifiers of the retrieved and newly requested data. The RSU then selects sender and receiver vehicles and corresponding data for V2V communication, while it simultaneously broadcasts a data item to vehicles that are instructed to tune into the I2V channel. The goal is to maximize the number of vehicles that retrieve their requested data. We prove that CDS is NP-hard by constructing a polynomial-time reduction from the Maximum Weighted Independent Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS and using a greedy method to approximately solve MWIS. We build a simulation model based on realistic traffic and communication characteristics and demonstrate the superiority and scalability of the proposed solution. The proposed model and solution, which are based on the centralized scheduler at the RSU, represent the first known vehicular ad hoc network (VANET) implementation of software defined network (SDN) concept.

Communication changes experienced by adults with cerebral palsy as they age

PURPOSE: Adults with cerebral palsy (CP) experience multiple, functional changes as they age, including changes to communication modes and methods that enable development and maintenance of relationships, communicative participation and quality-of-life. Little is known about the nature of communication changes experienced by this group. The aim of this study was to better understand how adults with CP experience changes in their communication abilities as they age and the subsequent psychosocial impact.

METHOD: Twenty adults with cerebral palsy aged 40-72 years with complex communication needs (CCN) participated in a series of in-depth interviews, framing their experiences of loss and grief throughout their lives. The impact of changing communication abilities emerged as an important area of focus. Data were analysed using constructivist grounded theory methodology.

RESULT: Themes arising from the participants' perceptions of their communication included experiencing communication change as a loss with subsequent impact on self-concept; and how communication is integral to the process of managing losses associated with older age.

CONCLUSION: Implications for speech-language pathologists working with older people with cerebral palsy and CCN include the need to understand the psychosocial impact of communication changes on social interaction, relationships and communicative participation. It is important to promote positive and meaningful communication options that maintain a coherent sense of self in addition to promoting functional communication skills and communicative participation.

Filtering redundant data from RFID data streams

Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.

Creating opportunities for interdisciplinary collaboration and patient-centred care: how nurses, doctors, pharmacists and patients use communication strategies when managing medications in an acute hospital setting.

AIMS AND OBJECTIVES: This paper examines the communication strategies that nurses, doctors, pharmacists and patients use when managing medications. BACKGROUND: Patient-centred medication management is best accomplished through interdisciplinary practice. Effective communication about managing medications between clinicians and patients has a direct influence on patient outcomes. There is a lack of research that adopts a multidisciplinary approach and involves critical in-depth analysis of medication interactions among nurses, doctors, pharmacists and patients. DESIGN: A critical ethnographic approach with video reflexivity was adopted to capture communication strategies during medication activities in two general medical wards of an acute care hospital in Melbourne, Australia. METHODS: A mixed ethnographic approach combining participant observations, field interviews, video recordings and video reflexive focus groups and interviews was employed. Seventy-six nurses, 31 doctors, 1 pharmacist and 27 patients gave written consent to participate in the study. Data analysis was informed by Fairclough's critical discourse analytic framework. FINDINGS: Clinicians' use of communication strategies was demonstrated in their interpersonal, authoritative and instructive talk with patients. Doctors adopted the language discourse of normalisation to standardise patients' illness experiences. Nurses and pharmacists employed the language discourses of preparedness and scrutiny to ensure that patient safety was maintained. Patients took up the discourse of politeness to raise medication concerns and question treatment decisions made by doctors, in their attempts to challenge decision-making about their health care treatment. In addition, the video method revealed clinicians' extensive use of body language in communication processes for medication management. CONCLUSIONS: The use of communication strategies by nurses, doctors, pharmacists and patients created opportunities for improved interdisciplinary collaboration and patient-centred medication management in an acute hospital setting. Language discourses shaped and were shaped by complex power relations between patients and clinicians and among clinicians themselves. RELEVANCE TO CLINICAL PRACTICE: Clinicians need to be encouraged to have regular conversations to talk about and challenge each other's practices. More emphasis should be placed on ensuring that patients are given opportunities to voice their concerns about how their medications are managed.

Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems

Machine-to-Machine (M2M) paradigm enables machines (sensors, actuators, robots, and smart meter readers) to communicate with each other with little or no human intervention. M2M is a key enabling technology for the cyber-physical systems (CPSs). This paper explores CPS beyond M2M concept and looks at futuristic applications. Our vision is CPS with distributed actuation and in-network processing. We describe few particular use cases that motivate the development of the M2M communication primitives tailored to large-scale CPS. M2M communications in literature were considered in limited extent so far. The existing work is based on small-scale M2M models and centralized solutions. Different sources discuss different primitives. Few existing decentralized solutions do not scale well. There is a need to design M2M communication primitives that will scale to thousands and trillions of M2M devices, without sacrificing solution quality. The main paradigm shift is to design localized algorithms, where CPS nodes make decisions based on local knowledge. Localized coordination and communication in networked robotics, for matching events and robots, were studied to illustrate new directions.

Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.

Doctor communication quality and friends' attitudes influence complementary medicine use in inflammatory bowel disease.

AIM: To examine the frequency of regular complementary and alternative therapy (CAM) use in three Australian cohorts of contrasting care setting and geography, and identify independent attitudinal and psychological predictors of CAM use across all cohorts. METHODS: A cross sectional questionnaire was administered to inflammatory bowel disease (IBD) patients in 3 separate cohorts which differed by geographical region and care setting. Demographics and frequency of regular CAM use were assessed, along with attitudes towards IBD medication and psychological parameters such as anxiety, depression, personality traits and quality of life (QOL), and compared across cohorts. Independent attitudinal and psychological predictors of CAM use were determined using binary logistic regression analysis. RESULTS: In 473 respondents (mean age 50.3 years, 60.2% female) regular CAM use was reported by 45.4%, and did not vary between cohorts. Only 54.1% of users disclosed CAM use to their doctor. Independent predictors of CAM use which confirm those reported previously were: covert conventional medication dose reduction (P < 0.001), seeking psychological treatment (P < 0.001), adverse effects of conventional medication (P = 0.043), and higher QOL (P < 0.001). Newly identified predictors were CAM use by family or friends (P < 0.001), dissatisfaction with patient-doctor communication (P < 0.001), and lower depression scores (P < 0.001). CONCLUSION: In addition to previously identified predictors of CAM use, these data show that physician attention to communication and the patient-doctor relationship is important as these factors influence CAM use. Patient reluctance to discuss CAM with physicians may promote greater reliance on social contacts to influence CAM decisions.

Graph theory based QoE-driven cooperation stimulation for content dissemination in device-To-device communication

With multimedia dominating the digital contents, Device-To-Device (D2D) communication has been proposed as a promising data offloading solution in the big data area. As the quality of experience (QoE) is a major determining factor in the success of new multimedia applications, we propose a QoEdriven cooperative content dissemination (QeCS) scheme in this work. Specifically, all users predict the QoE of the potential connections characterized by the mean opinion score (MOS), and send the results to the content provider (CP). Then CP formulates a weighted directed graph according to the network topology and MOS of each potential connection. In order to stimulate cooperation among the users, the content dissemination mechanism is designed through seeking 1-factor of the weighted directed graph with the maximum weight thus achieving maximum total user MOS. Additionally, a debt mechanism is adopted to combat the cheat attacks. Furthermore, we extend the proposed QeCS scheme by considering a constrained condition to the optimization problem for fairness improvement. Extensive simulation results demonstrate that the proposed QeCS scheme achieves both efficiency and fairness especially in large scale and density networks.

Trustworthiness-hypercube-based reliable communication in mobile social networks

In mobile social networks (MSNs), the routing packet is forwarded from any user of in a group to any user of the other group until it reaches the destination group - the group where the destination is located. However, it is inevitable that malicious groups could compromise the quality and reliability of data. To alleviate such effect, analyzing the trustworthiness of a group has a positive influence on the confidence with which a group conducts transactions with that group. In our previous work, the feature-based first-priority relation graph (FPRG) of MSNs is proposed, in which two vertices (groups) are connected iff they have a first-priority relationship. In this paper, the trustworthiness computation of a group is firstly presented in the algorithm TC (Trustworthiness Computing) based on the FPRG. The trustworthiness of a group is evaluated based on the trustworthiness of neighbors and the number of malicious users in the group. We then establish the Trustworthiness-Hypercube-based Reliable Communication (THRC) algorithm in MSNs. The algorithm THRC can provide an effective and reliable data delivery routing. Finally, we also give two scenario simulations to elaborate the processes of the trustworthiness computation and reliable communication.

PPM-HDA: privacy-preserving and multifunctional health data aggregation with fault tolerance

Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users' health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA⁺) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA^⊕ as an extension of MHDA⁺ to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users' privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA⁺ is significantly reduced with the assistance of CSs. Our MHDA^⊕ scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.