Jose Roberto Perez Cruz

Multi-Session Key Management Scheme for Multimedia Group Communications

The Internet 2 deployment introduces new capabilities, such as multi-party collaboration, high-scale multimedia assembly and multicast communication. For this reason, the research concerning security is facing new challenges. One such challenge is to create secure multi-session frameworks to ensure the confidentiality of exchanged information. In a multi-session environment, several users are joined at two or more work sessions simultaneously. The confidentiality in these environments can be achieved using cryptographic methods. Unfortunately, the key management, necessary for such environments, creates two main problems: a high complexity in key distribution and a high storage cost. In this paper, we propose an efficient multi-session key management mechanism for dynamic multimedia group communication. Our solution proposes a functional architecture that exploits the overlapping of the user sessions to reduce the redundancy in key distribution. The proposed key management makes use of two key generation strategies: a key derivation technique to reduce the rekey overhead and a pseudorandom number generator that allows the users to generate an independent key per cipher packet.

A Delayed Checkpoint Approach for Communication-Induced Checkpointing in Autonomic Computing

Although the initiative of Autonomic Computing was introduced a dozen years ago, several challenges remain open. One of these challenges is the efficient monitoring at runtime oriented to the detection, diagnosis, and repair of problems that result from failures or bugs in software and/or hardware components. For this purpose, Communication-induced Checkpointing (CIC) can be a useful tool. Communication-induced Checkpointing has been used to attack a wide range of problems that arise in distributed systems, such as rollback recovery, software debugging and software verification, among others. In CIC algorithms, an autonomic component (process) asynchronously cooperates by exchanging information on the application messages about saved local states called checkpoints. CIC aims to form global consistent snapshots by grouping checkpoints (one by each component) in a non-coordinated way. To achieve this, CIC solutions continuously monitor the exchanged control information to identify possible dangerous checkpointing patterns. When a dangerous pattern is identified, it is broken by locally triggering a forced checkpoint. Nevertheless, as we will show, not all forced checkpoints triggered by current solutions are necessary. In this paper, we present a delayed checkpoint approach suitable for autonomic computing that reduces forced checkpoints by establishing certain triggering rules that we call safe checkpoint conditions. Finally, some results are presented which show that our proposal is more efficient than other current solutions.

Data Alignment for Data Fusion in Wireless Multimedia Sensor Networks Based on M2M

Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

Self-healing in autonomic distributed systems based on delayed communication-induced checkpointing

An autonomic distributed system is composed of geographically distributed autonomic components. One open challenge in autonomic computing is the efficient monitoring at runtime oriented towards the collection of information, from which the system itself will detect, diagnose, and repair problems that result from failures in software and/or hardware components. For this purpose, communication-induced checkpointing CIC can be a useful tool. CIC aims to form global consistent snapshots from which the system can recover. To achieve this, CIC solutions monitor exchanged information among the processes to identify dangerous checkpointing patterns. When a dangerous pattern is identified, it is broken by locally triggering a forced checkpoint. Nevertheless, not all triggered forced checkpoints are necessary. In this paper, we present a delayed CIC approach that reduces forced checkpoints by using triggering rules called safe checkpoint conditions. Finally, we present simulation results that show that our proposal is more efficient than other current solutions.

Temporal data alignment and association for event-streaming in ubiquitous environments based on fuzzy-causal dependencies

Advances in sensors as well as wireless networks and ad-hoc networks have allowed new applications based on ubiquitous environments (UE) to be conceived. The deployment of a UE implies the exchange of numerous data streams asynchronously generated by multiple distributed sources. Such data must be fusioned at a certain stage. To perform the data fusion, the involved devices need to agree on some common temporal references of the whole system. To attain such references, the current solutions propose the use of centralized schemes and/or global references. Unfortunately, in a UE it is difficult to get global references mainly due to the asynchronous execution nature of the distributed systems. In this paper, we propose a distributed data alignment and association approach that establishes temporal references among the exchanged data streams, without requiring the use of synchronized clocks or centralized schemes. This is achieved by translating temporal/spatial references based on a time-line and physical locations to fuzzy-causal dependencies among streams. Through the establishment of the fuzzy causal dependencies, we infer a degree of temporal closeness among the data streams, which can be useful to correlate, filter or combine such data at a later processing.

Temporal Alignment Model for Data Streams in Wireless Sensor Networks Based on Causal Dependencies

New applications based on wireless sensor networks (WSN), such as person-locator services, harvest a large amount of data streams that are simultaneously generated by multiple distributed sources. Specifically, in a WSN this paradigm of data generation/transmission is known as event-streaming. In order to be useful, all the collected data must be aligned so that it can be fused at a later phase. To perform such alignment, the sensors need to agree on common temporal references. Unfortunately, this agreement is difficult to achieve mainly due to the lack of perfectly synchronized physical clocks and the asynchronous nature of the execution. Some solutions tackle the issue of the temporal alignment; however, they demand extra resources to the network deployment since they try to impose global references by using a centralized scheme. In this paper, we propose a temporal alignment model for data streams that identifies temporal relationships and which does not require the use of synchronized clocks, global references, centralized schemes, or additional synchronization signals. The identification of temporal relationships without the use of synchronized clocks is achieved by translating temporal dependencies based on a time-line to causal dependencies among streams. Finally, we show the viability and the effectiveness of the model by simulating it over a sensor network with multihop communication.New applications based on wireless sensor networks (WSN), such as person-locator services, harvest a large amount of data streams that are simultaneously generated by multiple distributed sources. Specifically, in a WSN this paradigm of data generation/transmission is known as event-streaming. In order to be useful, all the collected data must be aligned so that it can be fused at a later phase. To perform such alignment, the sensors need to agree on common temporal references. Unfortunately, this agreement is difficult to achieve mainly due to the lack of perfectly synchronized physical clocks and the asynchronous nature of the execution. Some solutions tackle the issue of the temporal alignment; however, they demand extra resources to the network deployment since they try to impose global references by using a centralized scheme. In this paper, we propose a temporal alignment model for data streams that identifies temporal relationships and which does not require the use of synchronized clocks, global references, centralized schemes, or additional synchronization signals. The identification of temporal relationships without the use of synchronized clocks is achieved by translating temporal dependencies based on a time-line to causal dependencies among streams. Finally, we show the viability and the effectiveness of the model by simulating it over a sensor network with multihop communication.